import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib as mpl
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder, StandardScaler
from torch.utils.data import Dataset, DataLoader

from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

df_train = pd.DataFrame(pd.read_csv('/content/drive/MyDrive/DL_Project/trainv1.csv'))
df_test = pd.DataFrame(pd.read_csv('/content/drive/MyDrive/DL_Project/testv1.csv'))
df_train

<ipython-input-4-cf622a5b4f5d>:1: DtypeWarning: Columns (26) have mixed types. Specify dtype option on import or set low_memory=False.
  df_train = pd.DataFrame(pd.read_csv('/content/drive/MyDrive/DL_Project/trainv1.csv'))

df_test

df_train.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 100000 entries, 0 to 99999
Data columns (total 28 columns):
 #   Column                    Non-Null Count   Dtype  
---  ------                    --------------   -----  
 0   ID                        100000 non-null  object 
 1   Customer_ID               100000 non-null  object 
 2   Month                     100000 non-null  object 
 3   Name                      90015 non-null   object 
 4   Age                       100000 non-null  object 
 5   SSN                       100000 non-null  object 
 6   Occupation                100000 non-null  object 
 7   Annual_Income             100000 non-null  object 
 8   Monthly_Inhand_Salary     84998 non-null   float64
 9   Num_Bank_Accounts         100000 non-null  int64  
 10  Num_Credit_Card           100000 non-null  int64  
 11  Interest_Rate             100000 non-null  int64  
 12  Num_of_Loan               100000 non-null  object 
 13  Type_of_Loan              88592 non-null   object 
 14  Delay_from_due_date       100000 non-null  int64  
 15  Num_of_Delayed_Payment    92998 non-null   object 
 16  Changed_Credit_Limit      100000 non-null  object 
 17  Num_Credit_Inquiries      98035 non-null   float64
 18  Credit_Mix                100000 non-null  object 
 19  Outstanding_Debt          100000 non-null  object 
 20  Credit_Utilization_Ratio  100000 non-null  float64
 21  Credit_History_Age        90970 non-null   object 
 22  Payment_of_Min_Amount     100000 non-null  object 
 23  Total_EMI_per_month       100000 non-null  float64
 24  Amount_invested_monthly   95521 non-null   object 
 25  Payment_Behaviour         100000 non-null  object 
 26  Monthly_Balance           98800 non-null   object 
 27  Credit_Score              100000 non-null  object 
dtypes: float64(4), int64(4), object(20)
memory usage: 21.4+ MB

for col in df_train.columns:
    unique_values = df_train[col].unique()
    print(f"Column: {col}")
    print(f"Unique Values (Top 20): {unique_values[:20]}")
    print(f"Total Unique Values: {len(unique_values)}")
    print("=" * 20)

Column: ID
Unique Values (Top 20): ['0x1602' '0x1603' '0x1604' '0x1605' '0x1606' '0x1607' '0x1608' '0x1609'
 '0x160e' '0x160f' '0x1610' '0x1611' '0x1612' '0x1613' '0x1614' '0x1615'
 '0x161a' '0x161b' '0x161c' '0x161d']
Total Unique Values: 100000
====================
Column: Customer_ID
Unique Values (Top 20): ['CUS_0xd40' 'CUS_0x21b1' 'CUS_0x2dbc' 'CUS_0xb891' 'CUS_0x1cdb'
 'CUS_0x95ee' 'CUS_0x284a' 'CUS_0x5407' 'CUS_0x4157' 'CUS_0xba08'
 'CUS_0xa66b' 'CUS_0xc0ab' 'CUS_0x3e45' 'CUS_0x6c66' 'CUS_0xff4'
 'CUS_0x33d2' 'CUS_0x6070' 'CUS_0xfdb' 'CUS_0x3553' 'CUS_0x4100']
Total Unique Values: 12500
====================
Column: Month
Unique Values (Top 20): ['January' 'February' 'March' 'April' 'May' 'June' 'July' 'August']
Total Unique Values: 8
====================
Column: Name
Unique Values (Top 20): ['Aaron Maashoh' nan 'Rick Rothackerj' 'Langep' 'Jasond' 'Deepaa' 'Np'
 'Nadiaq' 'Annk' 'Charlie Zhur' 'Jamesj' 'Saphirj' 'Soyoungd'
 'Harriet McLeodd' 'Sinead Carews' 'Poornimaf' 'Chalmersa' 'Parkm'
 'Patrickg' 'Laurence Frosty']
Total Unique Values: 10140
====================
Column: Age
Unique Values (Top 20): ['23' '-500' '28_' '28' '34' '54' '55' '21' '31' '33' '34_' '7580' '30'
 '30_' '24' '24_' '44' '45' '40' '41']
Total Unique Values: 1788
====================
Column: SSN
Unique Values (Top 20): ['821-00-0265' '#F%$D@*&8' '004-07-5839' '486-85-3974' '072-31-6145'
 '615-06-7821' '612-70-8987' '411-51-0676' '500-92-6408' '070-19-1622'
 '366-68-1681' '221-30-8554' '342-90-2649' '414-53-2918' '328-33-6328'
 '655-05-7666' '965-46-2491' '891-55-9364' '928-91-4452' '084-25-3745']
Total Unique Values: 12501
====================
Column: Occupation
Unique Values (Top 20): ['Scientist' '_______' 'Teacher' 'Engineer' 'Entrepreneur' 'Developer'
 'Lawyer' 'Media_Manager' 'Doctor' 'Journalist' 'Manager' 'Accountant'
 'Musician' 'Mechanic' 'Writer' 'Architect']
Total Unique Values: 16
====================
Column: Annual_Income
Unique Values (Top 20): ['19114.12' '34847.84' '34847.84_' '143162.64' '30689.89' '30689.89_'
 '35547.71_' '35547.71' '73928.46' '131313.4' '10909427.0' '34081.38_'
 '34081.38' '114838.41' '114838.41_' '31370.8' '33751.27' '88640.24'
 '88640.24_' '54392.16']
Total Unique Values: 18940
====================
Column: Monthly_Inhand_Salary
Unique Values (Top 20): [ 1824.84333333            nan  3037.98666667 12187.22
  2612.49083333  2853.30916667  5988.705      11242.78333333
 10469.20775939  2611.115       9843.8675      2825.23333333
  2948.60583333  7266.68666667  4766.68         519.12875
  2415.855       2942.14833333  7591.59        2898.385     ]
Total Unique Values: 13236
====================
Column: Num_Bank_Accounts
Unique Values (Top 20): [   3    2    1    7    4    0    8    5    6    9   10 1414 1231   67
  572 1488   91  528 1647 1696]
Total Unique Values: 943
====================
Column: Num_Credit_Card
Unique Values (Top 20): [   4 1385    5 1288    1    7    6 1029  488    8 1381  898    3  518
 1005    9 1327 1189    2   10]
Total Unique Values: 1179
====================
Column: Interest_Rate
Unique Values (Top 20): [   3    6    8    4    5 5318   15    7   12   20    1  433   14   32
   16   17 5240 4975   10   31]
Total Unique Values: 1750
====================
Column: Num_of_Loan
Unique Values (Top 20): ['4' '1' '3' '967' '-100' '0' '0_' '2' '3_' '2_' '7' '5' '5_' '6' '8' '8_'
 '9' '9_' '4_' '7_']
Total Unique Values: 434
====================
Column: Type_of_Loan
Unique Values (Top 20): ['Auto Loan, Credit-Builder Loan, Personal Loan, and Home Equity Loan'
 'Credit-Builder Loan' 'Auto Loan, Auto Loan, and Not Specified'
 'Not Specified' nan 'Credit-Builder Loan, and Mortgage Loan'
 'Not Specified, Auto Loan, and Student Loan'
 'Personal Loan, Debt Consolidation Loan, and Auto Loan'
 'Not Specified, and Payday Loan'
 'Credit-Builder Loan, Personal Loan, and Auto Loan'
 'Payday Loan, and Payday Loan'
 'Not Specified, Student Loan, and Personal Loan'
 'Personal Loan, Payday Loan, Student Loan, Auto Loan, Home Equity Loan, Student Loan, and Payday Loan'
 'Not Specified, Student Loan, Student Loan, Credit-Builder Loan, and Auto Loan'
 'Payday Loan, and Home Equity Loan'
 'Credit-Builder Loan, Not Specified, Mortgage Loan, Payday Loan, Credit-Builder Loan, and Personal Loan'
 'Mortgage Loan, Debt Consolidation Loan, Payday Loan, Auto Loan, and Not Specified'
 'Credit-Builder Loan, Mortgage Loan, Mortgage Loan, Credit-Builder Loan, and Student Loan'
 'Not Specified, Student Loan, and Student Loan'
 'Payday Loan, Not Specified, Credit-Builder Loan, Debt Consolidation Loan, Payday Loan, Not Specified, Student Loan, and Student Loan']
Total Unique Values: 6261
====================
Column: Delay_from_due_date
Unique Values (Top 20): [ 3 -1  5  6  8  7 13 10  0  4  9  1 12 11 30 31 34 27 14  2]
Total Unique Values: 73
====================
Column: Num_of_Delayed_Payment
Unique Values (Top 20): ['7' nan '4' '8_' '6' '1' '-1' '3_' '0' '8' '5' '3' '9' '12' '15' '17'
 '10' '2' '2_' '11']
Total Unique Values: 750
====================
Column: Changed_Credit_Limit
Unique Values (Top 20): ['11.27' '_' '6.27' '9.27' '5.42' '7.42' '6.42' '7.1' '11.1' '9.1' '1.99'
 '-2.01' '-1.01' '-3.01' '2.58' '10.14' '9.14' '9.34' '15.34' '8.34']
Total Unique Values: 4384
====================
Column: Num_Credit_Inquiries
Unique Values (Top 20): [4.000e+00 2.000e+00 3.000e+00       nan 5.000e+00 9.000e+00 8.000e+00
 7.000e+00 6.000e+00 0.000e+00 1.000e+00 1.000e+01 1.050e+03 1.100e+01
 1.200e+01 1.044e+03 1.700e+01 1.936e+03 1.300e+01 5.680e+02]
Total Unique Values: 1224
====================
Column: Credit_Mix
Unique Values (Top 20): ['_' 'Good' 'Standard' 'Bad']
Total Unique Values: 4
====================
Column: Outstanding_Debt
Unique Values (Top 20): ['809.98' '605.03' '1303.01' '632.46' '943.86' '548.2' '352.16' '1704.18'
 '1377.74' '421.43' '1328.93' '1328.93_' '950.36' '179.22' '2602.69'
 '758.44' '818.22' '1296.64' '1283.37' '1283.37_']
Total Unique Values: 13178
====================
Column: Credit_Utilization_Ratio
Unique Values (Top 20): [26.82261962 31.94496006 28.60935202 31.37786187 24.79734691 27.26225871
 22.53759303 23.9337948  24.46403064 38.55084843 33.22495079 39.18265566
 34.97789475 33.3810102  31.13170161 32.93385629 28.61673482 41.70257342
 26.51981539 39.50164811]
Total Unique Values: 100000
====================
Column: Credit_History_Age
Unique Values (Top 20): ['22 Years and 1 Months' nan '22 Years and 3 Months'
 '22 Years and 4 Months' '22 Years and 5 Months' '22 Years and 6 Months'
 '22 Years and 7 Months' '26 Years and 7 Months' '26 Years and 8 Months'
 '26 Years and 9 Months' '26 Years and 10 Months' '26 Years and 11 Months'
 '27 Years and 0 Months' '27 Years and 1 Months' '27 Years and 2 Months'
 '17 Years and 9 Months' '17 Years and 10 Months' '17 Years and 11 Months'
 '18 Years and 1 Months' '18 Years and 2 Months']
Total Unique Values: 405
====================
Column: Payment_of_Min_Amount
Unique Values (Top 20): ['No' 'NM' 'Yes']
Total Unique Values: 3
====================
Column: Total_EMI_per_month
Unique Values (Top 20): [4.95749492e+01 1.88162146e+01 2.46992319e+02 1.64154517e+01
 0.00000000e+00 1.50150000e+04 1.55150000e+04 1.37644605e+02
 9.11220179e+02 2.38340000e+04 3.26620000e+04 7.04783327e+01
 2.26892792e+02 4.66161291e+01 1.64150000e+04 6.50081743e+01
 1.35173371e+02 8.03570000e+04 1.24392082e+02 3.65481972e+01]
Total Unique Values: 14950
====================
Column: Amount_invested_monthly
Unique Values (Top 20): ['80.41529543900253' '118.28022162236736' '81.699521264648'
 '199.4580743910713' '41.420153086217326' '62.430172331195294'
 '178.3440674122349' '24.785216509052056' '104.291825168246'
 '40.39123782853101' '58.51597569589465' '99.30622796053305'
 '130.11542024292334' '43.477190144355745' '70.10177420755677'
 '218.90434353388733' '168.413702679309' '232.86038375993544' '__10000__'
 '825.2162699393922']
Total Unique Values: 91050
====================
Column: Payment_Behaviour
Unique Values (Top 20): ['High_spent_Small_value_payments' 'Low_spent_Large_value_payments'
 'Low_spent_Medium_value_payments' 'Low_spent_Small_value_payments'
 'High_spent_Medium_value_payments' '!@9#%8'
 'High_spent_Large_value_payments']
Total Unique Values: 7
====================
Column: Monthly_Balance
Unique Values (Top 20): ['312.49408867943663' '284.62916249607184' '331.2098628537912'
 '223.45130972736786' '341.48923103222177' '340.4792117872438'
 '244.5653167062043' '358.12416760938714' '470.69062692529184'
 '484.5912142650067' '466.46647639764313' '465.6762241330048'
 '444.8670318506144' '481.505261949182' '464.8806778859809'
 '356.07810855965045' '1043.3159778669492' '998.8692967863226'
 '715.741367403555' '426.5134106068658']
Total Unique Values: 98793
====================
Column: Credit_Score
Unique Values (Top 20): ['Good' 'Standard' 'Poor']
Total Unique Values: 3
====================

df_train.isnull().sum()

df_test.isnull().sum()

df_train['is_train'] = 1
df_test['is_train'] = 0

# Combining the datasets into one DataFrame
df_group = pd.concat([df_train, df_test], ignore_index=True)

df_group.describe(include="all").T

df_group.isnull().any()

credit_score_counts = df_group['Credit_Score'].value_counts()

colors = ['#FF9999', '#66B2FF', '#99FF99', '#FFCC99', '#C299FF']

plt.figure(figsize=(10, 8))
plt.bar(credit_score_counts.index, credit_score_counts.values, color=colors[:len(credit_score_counts)], edgecolor='black')
plt.title('Distribution of Target Variable: Credit_Score', fontsize=14)
plt.xlabel('Credit_Score', fontsize=12)
plt.ylabel('Count', fontsize=12)
plt.xticks(fontsize=10)
plt.yticks(fontsize=10)
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

colors = ['#FF5733', '#33FFBD', '#337BFF', '#FF33A1', '#A133FF']

plt.figure(figsize=(8, 6))
plt.pie(credit_score_counts.values, labels=credit_score_counts.index, colors=colors, autopct='%1.1f%%', startangle=90, pctdistance=0.85)


centre_circle = plt.Circle((0, 0), 0.70, fc='white')
plt.gca().add_artist(centre_circle)

plt.title('Distribution of Target Variable: Credit_Score', fontsize=14)
plt.show()

df_group['ID'].duplicated().sum()

0

df_group['Name'] = df_group.groupby('Customer_ID')['Name'].transform(lambda x: x.fillna(x.mode()[0]))

df_group['Age_Cleaned'] = pd.to_numeric(df_group['Age'].str.extract(r'(\d+)')[0], errors='coerce')
df_group['Age_Cleaned'] = df_group['Age_Cleaned'].fillna(df_group['Age_Cleaned'].median())
df_group['Age_Cleaned'] = df_group['Age_Cleaned'].astype(int)
print(df_group[['Age', 'Age_Cleaned']].head())

    Age  Age_Cleaned
0    23           23
1    23           23
2  -500          500
3    23           23
4    23           23

import seaborn as sns
import matplotlib.pyplot as plt

# Density Plot
plt.figure(figsize=(10, 5))
sns.kdeplot(df_group['Age_Cleaned'], shade=True, color='blue')
plt.title('Density Plot of Variable: Age', fontsize=14)
plt.xlabel('Age', fontsize=12)
plt.ylabel('Density', fontsize=12)
plt.grid(True)
plt.show()

# Violin Plot
plt.figure(figsize=(5, 5))
sns.violinplot(x=df_group['Age_Cleaned'], palette='muted')
plt.title('Violin Plot Distribution of Variable: Age', fontsize=14)
plt.xlabel('Age', fontsize=12)
plt.grid(True)
plt.show()

<ipython-input-18-3038c387d625>:6: FutureWarning: 

`shade` is now deprecated in favor of `fill`; setting `fill=True`.
This will become an error in seaborn v0.14.0; please update your code.

  sns.kdeplot(df_group['Age_Cleaned'], shade=True, color='blue')

<ipython-input-18-3038c387d625>:15: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.

  sns.violinplot(x=df_group['Age_Cleaned'], palette='muted')

import re

def is_valid_ssn(ssn):
    pattern = r'^\d{3}-\d{2}-\d{4}$'
    return bool(re.match(pattern, ssn))

df_group['Is_Valid_SSN'] = df_group['SSN'].apply(is_valid_ssn)

valid_count = df_group['Is_Valid_SSN'].value_counts()

labels = ['Valid', 'Invalid']
colors = ['#66CC66', '#FF6666']
plt.figure(figsize=(6, 6))
plt.pie(valid_count, labels=labels, autopct='%1.1f%%', startangle=90, colors=colors, wedgeprops={'edgecolor': 'black'})
plt.title(' SSN Valid vs Invalid')
plt.show()

df_group[df_group['Is_Valid_SSN'] == 0]['SSN']

df_group['SSN_Cleaned'] = df_group['SSN'].apply(lambda x: x if isinstance(x, str) and len(x.split('-')) == 3 and all(part.isdigit() for part in x.split('-')) and len(x.split('-')[0]) == 3 and len(x.split('-')[1]) == 2 and len(x.split('-')[2]) == 4 else np.nan)

df_group['SSN_Cleaned'] = df_group.groupby('Customer_ID')['SSN_Cleaned'].transform(lambda x: x.ffill().bfill())

df_group['Is_Valid_SSN'] = df_group['SSN_Cleaned'].notnull()

df_group['Is_Valid_SSN'].value_counts()

plt.figure(figsize=(15, 8))
df_group['Occupation'].value_counts().plot(kind='barh', color='skyblue', edgecolor='black')
plt.title('Distribution of Variable: Occupation', fontsize=16)
plt.xlabel('Count', fontsize=14)
plt.ylabel('Occupation', fontsize=14)
plt.grid(axis='x', linestyle='--', alpha=0.7)
plt.show()

df_group["Occupation"].value_counts()

df_group['Occupation'] = df_group['Occupation'].replace('_______', np.nan)
df_group['Occupation'] = df_group.groupby('Customer_ID')['Occupation'].transform(lambda x: x.fillna(method='ffill').fillna(method='bfill'))
df_group["Occupation"].value_counts()

<ipython-input-24-9ed0b60522c0>:2: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.
  df_group['Occupation'] = df_group.groupby('Customer_ID')['Occupation'].transform(lambda x: x.fillna(method='ffill').fillna(method='bfill'))

occupation_counts = df_group['Occupation'].value_counts()

plt.figure(figsize=(10, 6))
occupation_counts.plot(kind='barh', color='steelblue', edgecolor='black')
plt.title('Distribution of Variable: Occupation')
plt.xlabel('Count')
plt.ylabel('Occupation')
plt.show()

df_group['Monthly_Inhand_Salary'] = df_group.groupby('Customer_ID')['Monthly_Inhand_Salary'].transform(lambda x: x.fillna(method='ffill').fillna(method='bfill'))
df_group['Monthly_Inhand_Salary'].isnull().sum()

<ipython-input-26-1f58bad5e2a4>:1: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.
  df_group['Monthly_Inhand_Salary'] = df_group.groupby('Customer_ID')['Monthly_Inhand_Salary'].transform(lambda x: x.fillna(method='ffill').fillna(method='bfill'))

0

# Histogram
sns.histplot(df_group['Monthly_Inhand_Salary'], kde=True, bins=30, color='purple')
plt.title('Distribution of Variable: Monthly_Inhand_Salary')
plt.xlabel('Monthly_Inhand_Salary')
plt.ylabel('Count')
plt.grid(True)
plt.show()

plt.figure(figsize=(5, 5))
plt.boxplot(df_group['Monthly_Inhand_Salary'], vert=False, patch_artist=True)
plt.title('Boxplot Distribution of Variable: Monthly_Inhand_Salary')
plt.xlabel('Monthly_Inhand_Salary')
plt.grid(True)
plt.show()

df_group['Annual_Income_Cleaned'] = df_group['Annual_Income'].str.replace('_', '', regex=False).astype(float)

sns.histplot(df_group['Annual_Income_Cleaned'], kde=True, bins=30, color='purple')
plt.title('Distribution of Variable: Annual_Income')
plt.xlabel('Annual_Income')
plt.ylabel('Count')
plt.grid(True)
plt.show()

plt.figure(figsize=(5, 5))
plt.boxplot(df_group['Annual_Income_Cleaned'], vert=False, patch_artist=True)
plt.title('Boxplot Distribution of Variable: Annual_Income')
plt.xlabel('Annual_Income')
plt.grid(True)
plt.show()

income_ratio = df_group['Annual_Income_Cleaned'] / df_group['Monthly_Inhand_Salary']
print(income_ratio.describe())

count    150000.000000
mean         65.262083
std         801.323485
min           8.089821
25%          11.561407
50%          12.035127
75%          12.573912
max       54110.522117
dtype: float64

Q1 = income_ratio.quantile(0.25)
Q3 = income_ratio.quantile(0.75)
IQR = Q3 - Q1

upper_bound = Q3 + 1.5 * IQR
upper_bound

14.092668813782176

ann_module = df_group.groupby("Customer_ID")["Annual_Income_Cleaned"].agg(
    lambda x: x.mode().iloc[0])

df_group["Annual_Income_Cleaned"] = df_group.apply(
    lambda row: ann_module[row["Customer_ID"]]
    if row["Annual_Income_Cleaned"] > row["Monthly_Inhand_Salary"] * 14
    else row["Annual_Income_Cleaned"],
    axis=1
)

df_group['Num_of_Loan'].value_counts()

df_group['Num_of_Loan_Cleaned'] = df_group['Num_of_Loan'].str.replace(r'\D', '', regex=True).astype(int)

# Density Plot
plt.figure(figsize=(10, 6))
sns.kdeplot(df_group['Num_of_Loan_Cleaned'], fill=True, color='green')
plt.title('Density Plot of Variable: Num_of_Loan', fontsize=14)
plt.xlabel('Num_of_Loan', fontsize=12)
plt.ylabel('Density', fontsize=12)
plt.grid(True)
plt.show()

# Violin Plot
plt.figure(figsize=(8, 6))
sns.violinplot(x=df_group['Num_of_Loan_Cleaned'], color='purple')
plt.title('Violin Plot of Variable: Num_of_Loan', fontsize=14)
plt.xlabel('Num_of_Loan', fontsize=12)
plt.grid(True)
plt.show()

df_group[df_group['Num_of_Loan_Cleaned'] < 100]['Num_of_Loan_Cleaned'].value_counts().head(20).sort_index()

df_group.loc[df_group["Num_of_Loan_Cleaned"] > 9, "Num_of_Loan_Cleaned"] = np.nan

df_group["Num_of_Loan_Cleaned"] = df_group.groupby("Customer_ID")["Num_of_Loan_Cleaned"].transform(
    lambda x: x.fillna(method="ffill").fillna(method="bfill"))

<ipython-input-39-882e7865d870>:4: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.
  lambda x: x.fillna(method="ffill").fillna(method="bfill"))

sns.histplot(df_group['Num_of_Loan_Cleaned'], kde=True, bins=30, color='blue')
plt.title('Distribution of Variable: Num_of_Loan_Cleaned')
plt.xlabel('Num_of_Loan_Cleaned')
plt.ylabel('Count')
plt.grid(True)
plt.show()

plt.figure(figsize=(5, 5))
plt.boxplot(df_group['Num_of_Loan_Cleaned'], vert=False, patch_artist=True)
plt.title('Boxplot Distribution of Variable: Num_of_Loan_Cleaned')
plt.xlabel('Num_of_Loan_Cleaned')
plt.grid(True)
plt.show()

sns.histplot(df_group['Num_Bank_Accounts'], kde=True, bins=30, color='blue')
plt.title('Distribution of Variable: Num_Bank_Accounts')
plt.xlabel('Num_Bank_Accounts')
plt.ylabel('Count')
plt.grid(True)
plt.show()

plt.figure(figsize=(5, 5))
plt.boxplot(df_group['Num_Bank_Accounts'], vert=False, patch_artist=True)
plt.title('Boxplot Distribution of Variable: Num_Bank_Accounts')
plt.xlabel('Num_Bank_Accounts')
plt.grid(True)
plt.show()

df_group[df_group['Num_Bank_Accounts'] < 100]['Num_Bank_Accounts'].value_counts().head(20).sort_index()

df_group.loc[(df_group["Num_Bank_Accounts"] < 0) | (df_group["Num_Bank_Accounts"] > 10),
        "Num_Bank_Accounts"] = np.nan

df_group["Num_Bank_Accounts"] = df_group.groupby("Customer_ID")["Num_Bank_Accounts"].transform(
    lambda x: x.fillna(method="ffill").fillna(method="bfill"))

<ipython-input-43-eef4c02d9120>:5: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.
  lambda x: x.fillna(method="ffill").fillna(method="bfill"))

sns.histplot(df_group['Num_Bank_Accounts'], kde=True, bins=30, color='blue')
plt.title('Distribution of Variable: Num_Bank_Accounts')
plt.xlabel('Num_Bank_Accounts')
plt.ylabel('Count')
plt.grid(True)
plt.show()

plt.figure(figsize=(5, 5))
plt.boxplot(df_group['Num_Bank_Accounts'], vert=False, patch_artist=True)
plt.title('Boxplot Distribution of Variable: Num_Bank_Accounts')
plt.xlabel('Num_Bank_Accounts')
plt.grid(True)
plt.show()

sns.histplot(df_group['Num_Credit_Card'], kde=True, bins=30, color='blue')
plt.title('Distribution of Variable: Num_Credit_Card')
plt.xlabel('Num_Credit_Card')
plt.ylabel('Count')
plt.grid(True)
plt.show()

plt.figure(figsize=(5, 5))
plt.boxplot(df_group['Num_Credit_Card'], vert=False, patch_artist=True)
plt.title('Boxplot Distribution of Variable: Num_Credit_Card')
plt.xlabel('Num_Credit_Card')
plt.grid(True)
plt.show()

df_group[df_group['Num_Credit_Card'] < 100]['Num_Credit_Card'].value_counts().head(20).sort_index()

df_group.loc[(df_group["Num_Credit_Card"] < 1) | (df_group["Num_Credit_Card"] > 10),
        "Num_Credit_Card"] = np.nan

df_group["Num_Credit_Card"] = df_group.groupby("Customer_ID")["Num_Credit_Card"].transform(
    lambda x: x.fillna(method="ffill").fillna(method="bfill"))

<ipython-input-47-5754288aab31>:5: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.
  lambda x: x.fillna(method="ffill").fillna(method="bfill"))

sns.histplot(df_group['Num_Credit_Card'], kde=True, bins=30, color='blue')
plt.title('Distribution of Variable: Num_Credit_Card')
plt.xlabel('Num_Credit_Card')
plt.ylabel('Count')
plt.grid(True)
plt.show()

plt.figure(figsize=(5, 5))
plt.boxplot(df_group['Num_Credit_Card'], vert=False, patch_artist=True)
plt.title('Boxplot Distribution of Variable: Num_Credit_Card')
plt.xlabel('Num_Credit_Card')
plt.grid(True)
plt.show()

sns.histplot(df_group['Interest_Rate'], kde=True, bins=30, color='blue')
plt.title('Distribution of Variable: Interest_Rate')
plt.xlabel('Interest_Rate')
plt.ylabel('Count')
plt.grid(True)
plt.show()

plt.figure(figsize=(5, 5))
plt.boxplot(df_group['Interest_Rate'], vert=False, patch_artist=True)
plt.title('Boxplot Distribution of Variable: Interest_Rate')
plt.xlabel('Interest_Rate')
plt.grid(True)
plt.show()

df_group[df_group['Interest_Rate'] < 20]['Interest_Rate'].value_counts().head(20).sort_index()

filtered_interest_rate = df_group[(df_group['Interest_Rate'] > 20) & (df_group['Interest_Rate'] < 100)]
interest_rate_counts = filtered_interest_rate['Interest_Rate'].value_counts().head(20).sort_index()

print(interest_rate_counts)

Interest_Rate
21    2335
22    2580
23    2530
24    2533
25    2356
26    2238
27    2416
28    2431
29    2495
30    2536
31    2188
32    2616
33    2201
34    2246
59       2
75       2
76       2
81       1
89       2
95       2
Name: count, dtype: int64

df_group.loc[df_group["Interest_Rate"] > 34,
        "Interest_Rate"] = np.nan

df_group["Interest_Rate"] = df_group.groupby("Customer_ID")["Interest_Rate"].transform(
    lambda x: x.fillna(method="ffill").fillna(method="bfill"))

<ipython-input-52-fb42c8ef6ab2>:5: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.
  lambda x: x.fillna(method="ffill").fillna(method="bfill"))

sns.histplot(df_group['Interest_Rate'], kde=True, bins=30, color='blue')
plt.title('Distribution of Variable: Interest_Rate')
plt.xlabel('Interest_Rate')
plt.ylabel('Count')
plt.grid(True)
plt.show()

plt.figure(figsize=(5, 5))
plt.boxplot(df_group['Interest_Rate'], vert=False, patch_artist=True)
plt.title('Boxplot Distribution of Variable: Interest_Rate')
plt.xlabel('Interest_Rate')
plt.grid(True)
plt.show()

df_group['Type_of_Loan'].nunique()
print('\n', df_group['Type_of_Loan'].value_counts())

 Type_of_Loan
Not Specified                                                                                                                         2112
Credit-Builder Loan                                                                                                                   1920
Personal Loan                                                                                                                         1908
Debt Consolidation Loan                                                                                                               1896
Student Loan                                                                                                                          1860
                                                                                                                                      ... 
Not Specified, Mortgage Loan, Auto Loan, and Payday Loan                                                                                12
Payday Loan, Mortgage Loan, Debt Consolidation Loan, and Student Loan                                                                   12
Debt Consolidation Loan, Auto Loan, Personal Loan, Debt Consolidation Loan, Student Loan, and Credit-Builder Loan                       12
Student Loan, Auto Loan, Student Loan, Credit-Builder Loan, Home Equity Loan, Debt Consolidation Loan, and Debt Consolidation Loan      12
Personal Loan, Auto Loan, Mortgage Loan, Student Loan, and Student Loan                                                                 12
Name: count, Length: 6260, dtype: int64

df_group[df_group['Type_of_Loan'].isnull() == True][['Customer_ID', 'Type_of_Loan', 'Num_of_Loan_Cleaned']].head(10)

df_group['Type_of_Loan'].fillna('No Loan', inplace=True)
df_group["Type_of_Loan_Cleaned"] = df_group["Type_of_Loan"].str.replace(" and", "", regex=False)
df_group['Type_of_Loan_Cleaned'] = df_group['Type_of_Loan_Cleaned'].str.split(', ')

unique_loans = set()
for loans in df_group['Type_of_Loan_Cleaned']:
    for loan in loans:
        loan_items = [item.strip() for item in loan.split(",")]
        unique_loans.update(loan_items)

unique_loans = sorted(unique_loans)
unique_loans

<ipython-input-56-12ef8776b8f6>:1: FutureWarning: A value is trying to be set on a copy of a DataFrame or Series through chained assignment using an inplace method.
The behavior will change in pandas 3.0. This inplace method will never work because the intermediate object on which we are setting values always behaves as a copy.

For example, when doing 'df[col].method(value, inplace=True)', try using 'df.method({col: value}, inplace=True)' or df[col] = df[col].method(value) instead, to perform the operation inplace on the original object.


  df_group['Type_of_Loan'].fillna('No Loan', inplace=True)

['Auto Loan',
 'Credit-Builder Loan',
 'Debt Consolidation Loan',
 'Home Equity Loan',
 'Mortgage Loan',
 'No Loan',
 'Not Specified',
 'Payday Loan',
 'Personal Loan',
 'Student Loan']

df_group['Type_of_Loan_Cleaned'] = df_group['Type_of_Loan_Cleaned'].apply(
    lambda x: ', '.join(sorted(set(x)))
)

df_group['Type_of_Loan_Cleaned'].value_counts()

loan_counts = []
for loans in unique_loans:
    count_items = len(df_group[df_group['Type_of_Loan_Cleaned'].str.contains(loans, na=False)])
    loan_counts.append({'Type': loans, 'Count': count_items})

loan_counts

[{'Type': 'Auto Loan', 'Count': 45840},
 {'Type': 'Credit-Builder Loan', 'Count': 47592},
 {'Type': 'Debt Consolidation Loan', 'Count': 46560},
 {'Type': 'Home Equity Loan', 'Count': 47100},
 {'Type': 'Mortgage Loan', 'Count': 47040},
 {'Type': 'No Loan', 'Count': 17112},
 {'Type': 'Not Specified', 'Count': 47520},
 {'Type': 'Payday Loan', 'Count': 47916},
 {'Type': 'Personal Loan', 'Count': 46656},
 {'Type': 'Student Loan', 'Count': 46560}]

loans = pd.DataFrame(loan_counts)
plt.figure(figsize=(12, 6))
plt.bar(loans['Type'], loans['Count'], color='skyblue', edgecolor='black')
plt.title('Frequency of Loan Types', fontsize=16)
plt.xlabel('Loan Type', fontsize=14)
plt.ylabel('Count', fontsize=14)
plt.xticks(rotation=45)
plt.grid(axis='y', linestyle='--', alpha=0.7)
plt.show()

df_group['Delay_from_due_date'].value_counts()

negative_delays = df_group[df_group['Delay_from_due_date'] < 0]
print(negative_delays['Delay_from_due_date'].describe())

count    889.000000
mean      -2.271091
std        1.221830
min       -5.000000
25%       -3.000000
50%       -2.000000
75%       -1.000000
max       -1.000000
Name: Delay_from_due_date, dtype: float64

def check_delays_each_costumer(group):
    return (group['Delay_from_due_date'].diff().abs() > 30).any()

check = df_group.groupby("Customer_ID").apply(check_delays_each_costumer) == True
check.sum()

<ipython-input-62-21e4f303ec75>:4: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
  check = df_group.groupby("Customer_ID").apply(check_delays_each_costumer) == True

0

# Line Plot
plt.figure(figsize=(10, 6))
delay_counts = df_group['Delay_from_due_date'].value_counts().sort_index()
plt.plot(delay_counts.index, delay_counts.values, marker='o', color='green')
plt.title('Line Plot of Variable: Delay_from_due_date', fontsize=14)
plt.xlabel('Delay_from_due_date', fontsize=12)
plt.ylabel('Count', fontsize=12)
plt.grid(True)
plt.show()

# Violin Plot
plt.figure(figsize=(8, 6))
sns.violinplot(x=df_group['Delay_from_due_date'], color='purple')
plt.title('Violin Plot of Variable: Delay_from_due_date', fontsize=14)
plt.xlabel('Delay_from_due_date', fontsize=12)
plt.grid(True)
plt.show()

df_group['Num_of_Delayed_Payment_Cleaned'] = df_group['Num_of_Delayed_Payment'].str.replace('_', '', regex=False).astype(float)
df_group[df_group['Num_of_Delayed_Payment_Cleaned'] < 10]['Num_of_Delayed_Payment_Cleaned'].value_counts().head()

df_group[df_group['Num_of_Delayed_Payment_Cleaned'] > 25]['Num_of_Delayed_Payment_Cleaned'].value_counts().head(15)

df_group.loc[(df_group["Num_of_Delayed_Payment_Cleaned"] < 0) | (df_group["Num_of_Delayed_Payment_Cleaned"] > 28),
        "Num_of_Delayed_Payment_Cleaned"] = np.nan

df_group["Num_of_Delayed_Payment_Cleaned"] = df_group.groupby("Customer_ID")["Num_of_Delayed_Payment_Cleaned"].transform(
    lambda x: x.fillna(method="ffill").fillna(method="bfill"))

# Histogram
plt.hist(df_group["Num_of_Delayed_Payment_Cleaned"], bins=10, edgecolor='black')
plt.title('Distribution of Variable: Num_of_Delayed_Payment_Cleaned')
plt.xlabel('Num_of_Delayed_Payment_Cleaned')
plt.ylabel('Count')
plt.grid(True)
plt.show()

plt.figure(figsize=(5, 5))
plt.boxplot(df_group["Num_of_Delayed_Payment_Cleaned"], vert=False, patch_artist=True)
plt.title('Boxplot Distribution of Variable: Num_of_Delayed_Payment_Cleaned')
plt.xlabel('Num_of_Delayed_Payment_Cleaned')
plt.grid(True)
plt.show()

<ipython-input-66-a19df7a99c39>:5: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.
  lambda x: x.fillna(method="ffill").fillna(method="bfill"))

df_group['Changed_Credit_Limit'].value_counts().head(50)

df_group.loc[(df_group["Changed_Credit_Limit"] == "_"), "Changed_Credit_Limit"] = np.nan

df_group['Changed_Credit_Limit_Cleaned'] = pd.to_numeric(
    df_group['Changed_Credit_Limit'].str.replace('_', '', regex=False),
    errors='coerce'
)

df_group["Changed_Credit_Limit_Cleaned"] = df_group.groupby("Customer_ID")["Changed_Credit_Limit_Cleaned"].transform(
    lambda x: x.fillna(method="ffill").fillna(method="bfill"))

df_group['Changed_Credit_Limit_Cleaned'] = df_group['Changed_Credit_Limit_Cleaned'].round(3)
df_group['Changed_Credit_Limit_Cleaned'].value_counts()

<ipython-input-69-c3734b7ef243>:9: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.
  lambda x: x.fillna(method="ffill").fillna(method="bfill"))

# Histogram
sns.histplot(df_group['Changed_Credit_Limit_Cleaned'], kde=True, bins=30, color='lime')
plt.title('Distribution Distribution of Variable: Changed_Credit_Limit')
plt.xlabel('Changed_Credit_Limit')
plt.ylabel('Count')
plt.grid(True)
plt.show()

plt.figure(figsize=(5, 5))
plt.boxplot(df_group['Changed_Credit_Limit_Cleaned'], vert=False, patch_artist=True)
plt.title('Boxplot Distribution of Variable: Changed_Credit_Limit')
plt.xlabel('Changed_Credit_Limit')
plt.grid(True)
plt.show()

plt.figure(figsize=(5, 5))
plt.boxplot(df_group['Num_Credit_Inquiries'].dropna(), vert=False, patch_artist=True)
plt.title('Boxplot Distribution of Variable: Num_Credit_Inquiries')
plt.xlabel('Num_Credit_Inquiries')
plt.grid(True)
plt.show()

df_group[df_group['Num_Credit_Inquiries'] > 10]['Num_Credit_Inquiries'].value_counts().head(10)

df_group.loc[(df_group["Num_Credit_Inquiries"] > 17), "Num_Credit_Inquiries"] = np.nan

df_group["Num_Credit_Inquiries"] = df_group.groupby("Customer_ID")["Num_Credit_Inquiries"].transform(
    lambda x: x.fillna(method="ffill").fillna(method="bfill"))

sns.histplot(df_group['Num_Credit_Inquiries'], kde=True, bins=30, color='lime')
plt.title('Distribution Distribution of Variable: Num_Credit_Inquiries')
plt.xlabel('Num_Credit_Inquiries')
plt.ylabel('Count')
plt.grid(True)
plt.show()

plt.figure(figsize=(5, 5))
plt.boxplot(df_group['Num_Credit_Inquiries'], vert=False, patch_artist=True)
plt.title('Boxplot Distribution of Variable: Num_Credit_Inquiries')
plt.xlabel('Num_Credit_Inquiries')
plt.grid(True)
plt.show()

<ipython-input-73-9d75bb1fb014>:4: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.
  lambda x: x.fillna(method="ffill").fillna(method="bfill"))

plt.figure(figsize=(8, 6))
sns.countplot(x='Credit_Mix', data=df_group, palette='pastel')
plt.title('Distribution of Target Variable: Credit_Mix')
plt.xlabel('Credit_Mix')
plt.ylabel('Count')
plt.show()

<ipython-input-74-613dbba4248f>:2: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x='Credit_Mix', data=df_group, palette='pastel')

df_group.loc[(df_group["Credit_Mix"] == '_'), "Credit_Mix"] = np.nan

df_group["Credit_Mix"] = df_group.groupby("Customer_ID")["Credit_Mix"].transform(
    lambda x: x.fillna(x.mode()[0]))

plt.figure(figsize=(8, 6))
sns.countplot(x='Credit_Mix', data=df_group, palette='pastel')
plt.title('Distribution of Target Variable: Credit_Mix')
plt.xlabel('Credit_Mix')
plt.ylabel('Count')
plt.show()

<ipython-input-75-9698a37e768e>:7: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x='Credit_Mix', data=df_group, palette='pastel')

df_group['Outstanding_Debt_Cleaned'] = pd.to_numeric(
    df_group['Outstanding_Debt'].str.replace('_', '', regex=False),
    errors='coerce'
)

# Histogram
sns.histplot(df_group['Outstanding_Debt_Cleaned'], kde=True, bins=30, color='yellow')
plt.title('Distribution of Variable: Outstanding_Debt')
plt.xlabel('Outstanding_Debt')
plt.ylabel('Count')
plt.grid(True)
plt.show()

plt.figure(figsize=(5, 5))
plt.boxplot(df_group['Outstanding_Debt_Cleaned'], vert=False, patch_artist=True)
plt.title('Boxplot Distribution of Variable: Outstanding_Debt')
plt.xlabel('Outstanding_Debt')
plt.grid(True)
plt.show()

# Histogram
sns.histplot(df_group['Credit_Utilization_Ratio'], kde=True, bins=30, color='blue')
plt.title('Distribution Credit_Utilization_Ratio in Months')
plt.xlabel('Credit_Utilization_Ratio')
plt.ylabel('Count')
plt.grid(True)
plt.show()

plt.figure(figsize=(5, 5))
plt.boxplot(df_group['Credit_Utilization_Ratio'], vert=False, patch_artist=True)
plt.title('Boxplot Distribution of Variable: Credit_Utilization_Ratio')
plt.xlabel('Credit_Utilization_Ratio')
plt.grid(True)
plt.show()

def convert_to_months(value):
    if pd.isnull(value):
        return np.nan
    match = re.match(r'(\d+) Years and (\d+) Months', value)
    if match:
        years, months = int(match.group(1)), int(match.group(2))
        return years * 12 + months
    return np.nan

df_group['Credit_History_Age_in_Months'] = df_group['Credit_History_Age'].apply(convert_to_months)

df_group["Credit_History_Age_in_Months"].value_counts()

df_group.groupby("Customer_ID")["Credit_History_Age_in_Months"].value_counts(
    dropna=False).head(20)

def fill_nan_credit(df):
    # Forward fill: Isi NaN dengan nilai sebelumnya + 1
    df['Credit_History_Age_in_Months'] = df['Credit_History_Age_in_Months'].fillna(method='ffill') + 1
    # Backward fill: Isi NaN yang tersisa dengan nilai berikutnya - 1
    df['Credit_History_Age_in_Months'] = df['Credit_History_Age_in_Months'].fillna(method='bfill') - 1
    return df

df_group = df_group.groupby("Customer_ID", group_keys=False).apply(fill_nan_credit)
df_group['Credit_History_Age_in_Months'].isnull().sum()

<ipython-input-81-472e60c0e87f>:3: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.
  df['Credit_History_Age_in_Months'] = df['Credit_History_Age_in_Months'].fillna(method='ffill') + 1
<ipython-input-81-472e60c0e87f>:5: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.
  df['Credit_History_Age_in_Months'] = df['Credit_History_Age_in_Months'].fillna(method='bfill') - 1
<ipython-input-81-472e60c0e87f>:8: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
  df_group = df_group.groupby("Customer_ID", group_keys=False).apply(fill_nan_credit)

0

plt.figure(figsize=(10, 5))
sns.histplot(df_group['Credit_History_Age_in_Months'], kde=True, bins=30, color='red')
plt.title('Distribution Credit History Age in Months')
plt.xlabel('Total Months')
plt.ylabel('Count')
plt.grid(True)
plt.show()

plt.figure(figsize=(5, 5))
plt.boxplot(df_group['Credit_History_Age_in_Months'], vert=False, patch_artist=True)
plt.title('Boxplot Credit History Age In Months')
plt.xlabel('Total Months')
plt.grid(True)
plt.show()

plt.figure(figsize=(8, 6))
sns.countplot(x='Payment_of_Min_Amount', data=df_group, palette='pastel')
plt.title('Distribution of Target Variable: Payment_of_Min_Amount')
plt.xlabel('Payment_of_Min_Amount')
plt.ylabel('Count')
plt.show()

<ipython-input-83-2601dd4ddbec>:2: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x='Payment_of_Min_Amount', data=df_group, palette='pastel')

df_group.groupby("Customer_ID")["Payment_of_Min_Amount"].value_counts(
    dropna=False).head(20)

def replace_nm_with_majority(group):
    majority_value = group['Payment_of_Min_Amount'][group['Payment_of_Min_Amount'] != 'NM'].mode()[0]
    group['Payment_of_Min_Amount'] = group['Payment_of_Min_Amount'].replace('NM', majority_value)
    return group

df_group = df_group.groupby('Customer_ID', group_keys=False).apply(replace_nm_with_majority)
df_group["Payment_of_Min_Amount"].isnull().sum()

<ipython-input-85-0baa9dd83691>:6: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
  df_group = df_group.groupby('Customer_ID', group_keys=False).apply(replace_nm_with_majority)

0

plt.figure(figsize=(8, 6))
sns.countplot(x='Payment_of_Min_Amount', data=df_group, palette='pastel')
plt.title('Distribution of Target Variable: Payment_of_Min_Amount')
plt.xlabel('Payment_of_Min_Amount')
plt.ylabel('Count')
plt.show()

<ipython-input-86-2601dd4ddbec>:2: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x='Payment_of_Min_Amount', data=df_group, palette='pastel')

plt.figure(figsize=(5, 5))
plt.boxplot(df_group['Total_EMI_per_month'], vert=False, patch_artist=True)
plt.title('Boxplot Total_EMI_per_month')
plt.xlabel('Total_EMI_per_month')
plt.grid(True)
plt.show()

df_group['Total_EMI_per_month'].value_counts()

df_group.loc[df_group["Total_EMI_per_month"] > df_group["Monthly_Inhand_Salary"] * 0.3]["Total_EMI_per_month"].value_counts()

df_group.loc[df_group["Total_EMI_per_month"] > df_group["Monthly_Inhand_Salary"] * 0.3, "Total_EMI_per_month"] = np.nan

df_group["Total_EMI_per_month"] = df_group.groupby(
    "Customer_ID")["Total_EMI_per_month"].transform(
        lambda x: x.fillna(x.mode()[0]) if not x.mode().empty else x)

def fix_inconsistent_emi(group):
    # Hitung nilai modus (mode) untuk setiap grup customer_id
    mode_value = group['Total_EMI_per_month'].mode()
    if not mode_value.empty:
        # Ganti nilai yang hanya muncul 1 kali dan bukan nilai pertama atau terakhir
        group['Total_EMI_per_month'] = group['Total_EMI_per_month'].apply(
            lambda x: mode_value[0] if group['Total_EMI_per_month'].value_counts()[x] == 1 and
            x != group['Total_EMI_per_month'].iloc[0] and x != group['Total_EMI_per_month'].iloc[-1] else x
        )
    return group

df_group = df_group.groupby("Customer_ID", group_keys=False).apply(fix_inconsistent_emi)

plt.figure(figsize=(10, 5))
sns.histplot(df_group["Total_EMI_per_month"], kde=True, bins=30, color='skyblue')
plt.title('Distribution Amount Invested Monthly')
plt.xlabel('Amount Invested Monthly')
plt.ylabel('Frequency')
plt.grid(True)
plt.show()

plt.figure(figsize=(5, 5))
plt.boxplot(df_group['Total_EMI_per_month'], vert=False, patch_artist=True)
plt.title('Boxplot Total_EMI_per_month')
plt.xlabel('Total_EMI_per_month')
plt.grid(True)
plt.show()

<ipython-input-90-f10def76ad72>:18: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.
  df_group = df_group.groupby("Customer_ID", group_keys=False).apply(fix_inconsistent_emi)

df_group['Amount_invested_monthly'].value_counts(dropna=False)

df_group['Amount_invested_monthly_cleaned'] = (
    df_group['Amount_invested_monthly']
    .str.replace('__', '', regex=False)
    .astype(float)
)

df_group["Amount_invested_monthly_cleaned"] = df_group.groupby("Customer_ID")["Amount_invested_monthly_cleaned"].transform(
        lambda x: x.fillna(method="ffill").fillna(method="bfill"))

df_group['Amount_invested_monthly_cleaned'] = df_group['Amount_invested_monthly_cleaned'].round(3)

# Histogram
plt.figure(figsize=(10, 5))
sns.histplot(df_group["Amount_invested_monthly_cleaned"], kde=True, bins=30, color='skyblue')
plt.title('Distribution Amount Invested Monthly')
plt.xlabel('Amount Invested Monthly')
plt.ylabel('Frequency')
plt.grid(True)
plt.show()

# Boxplot
plt.figure(figsize=(5, 5))
plt.boxplot(df_group["Amount_invested_monthly_cleaned"], vert=False, patch_artist=True)
plt.title('Boxplot Amount Invested Monthly')
plt.xlabel('Amount')
plt.grid(True)
plt.show()

<ipython-input-92-1834a8f96089>:8: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.
  lambda x: x.fillna(method="ffill").fillna(method="bfill"))

plt.figure(figsize=(10, 6))
sns.countplot(y='Payment_Behaviour', data=df_group, palette='pastel', orient='h')
plt.title('Distribution of Target Variable: Payment_Behaviour')
plt.xlabel('Count')
plt.show()

<ipython-input-93-bb439be2e0aa>:2: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(y='Payment_Behaviour', data=df_group, palette='pastel', orient='h')

df_group.groupby(["Customer_ID"])["Payment_Behaviour"].nunique().head(20)

df_group.loc[(df_group["Payment_Behaviour"] == "!@9#%8"), "Payment_Behaviour"] = np.nan

df_group["Payment_Behaviour"] = df_group.groupby("Customer_ID")["Payment_Behaviour"].transform(
        lambda x: x.fillna(method="ffill").fillna(method="bfill"))

plt.figure(figsize=(10, 6))
sns.countplot(y='Payment_Behaviour', data=df_group, palette='pastel', orient='h')
plt.title('Distribution of Target Variable: Payment_Behaviour')
plt.xlabel('Count')
plt.show()

<ipython-input-95-25f2ac98a2ae>:4: FutureWarning: Series.fillna with 'method' is deprecated and will raise in a future version. Use obj.ffill() or obj.bfill() instead.
  lambda x: x.fillna(method="ffill").fillna(method="bfill"))
<ipython-input-95-25f2ac98a2ae>:7: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(y='Payment_Behaviour', data=df_group, palette='pastel', orient='h')

df_group['Monthly_Balance'].value_counts(dropna=False)

df_group[df_group['Monthly_Balance'].str.contains('__', na=False)]['Monthly_Balance'].value_counts()

df_group['Monthly_Balance_Cleaned'] = df_group['Monthly_Balance'].replace(
    "__-333333333333333333333333333__", 0)

df_group['Monthly_Balance_Cleaned'] = pd.to_numeric(df_group['Monthly_Balance_Cleaned'], errors='coerce')
df_group['Monthly_Balance_Cleaned'] = df_group['Monthly_Balance_Cleaned'].round(3)

customer = df_group[df_group['Monthly_Balance_Cleaned'] == 0]
df_group[df_group['Customer_ID'].isin(customer['Customer_ID'])][['Customer_ID', 'Monthly_Balance_Cleaned']].head(10)

df_group.loc[df_group["Monthly_Balance_Cleaned"] == 0, "Monthly_Balance_Cleaned"] = df_group.loc[df_group["Monthly_Balance_Cleaned"] == 0, "Customer_ID"].map(
        df_group[df_group["Monthly_Balance_Cleaned"] != 0].groupby(
            "Customer_ID")["Monthly_Balance_Cleaned"].mean())

df_group["Monthly_Balance_Cleaned"] = df_group.groupby(
    "Customer_ID")["Monthly_Balance_Cleaned"].transform(lambda x: x.fillna(x.mean()))

plt.figure(figsize=(10, 5))
sns.histplot(df_group["Monthly_Balance_Cleaned"], kde=True, bins=30, color='green')
plt.title('Distribusi Monthly Balance')
plt.xlabel('Monthly Balance')
plt.ylabel('Frequency')
plt.grid(True)
plt.show()

plt.figure(figsize=(5, 5))
plt.boxplot(df_group["Monthly_Balance_Cleaned"], vert=False, patch_artist=True)
plt.title('Boxplot Monthly Balance')
plt.xlabel('Monthly Balance')
plt.grid(True)
plt.show()

df_group = df_group.drop(columns=['Age','SSN', 'Is_Valid_SSN', 'Annual_Income', 'Num_of_Loan', 'Type_of_Loan', 'Num_of_Delayed_Payment', 'Changed_Credit_Limit', 'Outstanding_Debt', 'Credit_History_Age', 'Amount_invested_monthly', 'Monthly_Balance'])
df_group.info()

<class 'pandas.core.frame.DataFrame'>
Index: 150000 entries, 0 to 149999
Data columns (total 29 columns):
 #   Column                           Non-Null Count   Dtype  
---  ------                           --------------   -----  
 0   ID                               150000 non-null  object 
 1   Customer_ID                      150000 non-null  object 
 2   Month                            150000 non-null  object 
 3   Name                             150000 non-null  object 
 4   Occupation                       150000 non-null  object 
 5   Monthly_Inhand_Salary            150000 non-null  float64
 6   Num_Bank_Accounts                150000 non-null  float64
 7   Num_Credit_Card                  150000 non-null  float64
 8   Interest_Rate                    150000 non-null  float64
 9   Delay_from_due_date              150000 non-null  int64  
 10  Num_Credit_Inquiries             150000 non-null  float64
 11  Credit_Mix                       150000 non-null  object 
 12  Credit_Utilization_Ratio         150000 non-null  float64
 13  Payment_of_Min_Amount            150000 non-null  object 
 14  Total_EMI_per_month              150000 non-null  float64
 15  Payment_Behaviour                150000 non-null  object 
 16  Credit_Score                     100000 non-null  object 
 17  is_train                         150000 non-null  int64  
 18  Age_Cleaned                      150000 non-null  int64  
 19  SSN_Cleaned                      150000 non-null  object 
 20  Annual_Income_Cleaned            150000 non-null  float64
 21  Num_of_Loan_Cleaned              150000 non-null  float64
 22  Type_of_Loan_Cleaned             150000 non-null  object 
 23  Num_of_Delayed_Payment_Cleaned   150000 non-null  float64
 24  Changed_Credit_Limit_Cleaned     150000 non-null  float64
 25  Outstanding_Debt_Cleaned         150000 non-null  float64
 26  Credit_History_Age_in_Months     150000 non-null  float64
 27  Amount_invested_monthly_cleaned  150000 non-null  float64
 28  Monthly_Balance_Cleaned          150000 non-null  float64
dtypes: float64(15), int64(3), object(11)
memory usage: 38.4+ MB

df_group

plt.figure(figsize=(20, 40))

numeric = df_group.select_dtypes(include=['number']).columns # Select numerical columns


for i, col in enumerate(numeric):
    plt.subplot(9, 3, i + 1)
    sns.boxplot(x='Credit_Score', y=col, data=df_group, palette='Set1')
    plt.title(f'Boxplot of {col} by Credit Score', fontsize=13, pad=10)
    plt.xlabel('Credit Score', fontsize=13)
    plt.ylabel(col, fontsize=13)
    plt.xticks(fontsize=12)
    plt.yticks(fontsize=12)

    min_val = df_group[col].min()
    max_val = df_group[col].max()
    margin = (max_val - min_val) * 0.1
    plt.ylim(min_val - margin, max_val + margin)

plt.subplots_adjust(wspace=0.4, hspace=0.7)
plt.tight_layout()
plt.show()

<ipython-input-102-a2600b2eb564>:8: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x='Credit_Score', y=col, data=df_group, palette='Set1')
<ipython-input-102-a2600b2eb564>:8: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x='Credit_Score', y=col, data=df_group, palette='Set1')
<ipython-input-102-a2600b2eb564>:8: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x='Credit_Score', y=col, data=df_group, palette='Set1')
<ipython-input-102-a2600b2eb564>:8: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x='Credit_Score', y=col, data=df_group, palette='Set1')
<ipython-input-102-a2600b2eb564>:8: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x='Credit_Score', y=col, data=df_group, palette='Set1')
<ipython-input-102-a2600b2eb564>:8: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x='Credit_Score', y=col, data=df_group, palette='Set1')
<ipython-input-102-a2600b2eb564>:8: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x='Credit_Score', y=col, data=df_group, palette='Set1')
<ipython-input-102-a2600b2eb564>:8: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x='Credit_Score', y=col, data=df_group, palette='Set1')
<ipython-input-102-a2600b2eb564>:8: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x='Credit_Score', y=col, data=df_group, palette='Set1')
<ipython-input-102-a2600b2eb564>:8: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x='Credit_Score', y=col, data=df_group, palette='Set1')
<ipython-input-102-a2600b2eb564>:8: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x='Credit_Score', y=col, data=df_group, palette='Set1')
<ipython-input-102-a2600b2eb564>:8: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x='Credit_Score', y=col, data=df_group, palette='Set1')
<ipython-input-102-a2600b2eb564>:8: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x='Credit_Score', y=col, data=df_group, palette='Set1')
<ipython-input-102-a2600b2eb564>:8: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x='Credit_Score', y=col, data=df_group, palette='Set1')
<ipython-input-102-a2600b2eb564>:8: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x='Credit_Score', y=col, data=df_group, palette='Set1')
<ipython-input-102-a2600b2eb564>:8: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x='Credit_Score', y=col, data=df_group, palette='Set1')
<ipython-input-102-a2600b2eb564>:8: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x='Credit_Score', y=col, data=df_group, palette='Set1')
<ipython-input-102-a2600b2eb564>:8: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.boxplot(x='Credit_Score', y=col, data=df_group, palette='Set1')

import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns

numeric = df_group.select_dtypes(exclude=object).columns

fig, ax = plt.subplots(figsize=(20, 35))
fig.patch.set_facecolor('white')

mpl.rcParams['font.family'] = 'DejaVu Sans'
mpl.rcParams['font.size'] = 12
colors = sns.color_palette("tab10", n_colors=len(numeric)).as_hex()

for i, col in enumerate(numeric):
    plt.subplot(9, 3, i + 1)
    sns.kdeplot(x=df_group[col], color=colors[i], fill=True)
    plt.title(f'Distribution of {col}', fontsize=10)
    plt.grid(visible=False)

plt.tight_layout()
plt.show()

import numpy as np

plt.figure(figsize=(10, len(numeric) * 4))
for i, col in enumerate(numeric):
    try:
        if df_group[col].nunique() > 1:  # Ensure column has sufficient variance
            plt.subplot(len(numeric), 1, i + 1)
            sns.histplot(
                data=df_group,
                x=col,
                hue='Credit_Score',
                kde=True,
                bins=30,
                palette='Set2'
            )
            plt.title(f'Distribution of {col} by Credit Score', fontsize=13, pad=10)
            plt.xlabel(col, fontsize=13)
            plt.ylabel('Frequency', fontsize=13)
            plt.xticks(fontsize=12)
            plt.yticks(fontsize=12)
        else:
            print(f"Skipping column '{col}' due to insufficient variance.")
    except np.linalg.LinAlgError:
        print(f"KDE failed for column '{col}', disabling KDE.")
        plt.subplot(len(numeric), 1, i + 1)
        sns.histplot(
            data=df_group,
            x=col,
            hue='Credit_Score',
            kde=False,  # Disable KDE if it fails
            bins=30,
            palette='Set2'
        )
        plt.title(f'Distribution of {col} by Credit Score (KDE Disabled)', fontsize=13, pad=10)
        plt.xlabel(col, fontsize=13)
        plt.ylabel('Frequency', fontsize=13)
        plt.xticks(fontsize=12)
        plt.yticks(fontsize=12)

plt.subplots_adjust(wspace=0.4, hspace=0.7)
plt.tight_layout()
plt.show()

KDE failed for column 'is_train', disabling KDE.

plt.figure(figsize=(17,10))
cor = df_group[numeric].corr(method="kendall")
sns.heatmap(cor, annot=True)
plt.title('"Correlation of Numeric Features"')
plt.show()

object_columns = df_group.select_dtypes(include=['object']).columns
object_columns = object_columns[object_columns != 'test_yes']
print(object_columns)

Index(['ID', 'Customer_ID', 'Month', 'Name', 'Occupation', 'Credit_Mix',
       'Payment_of_Min_Amount', 'Payment_Behaviour', 'Credit_Score',
       'SSN_Cleaned', 'Type_of_Loan_Cleaned'],
      dtype='object')

for col in object_columns:
    unique_values = df_group[col].nunique()
    print(f"Different and unique columns {col}: ({unique_values})")

Different and unique columns ID: (150000)
Different and unique columns Customer_ID: (12500)
Different and unique columns Month: (12)
Different and unique columns Name: (10139)
Different and unique columns Occupation: (15)
Different and unique columns Credit_Mix: (3)
Different and unique columns Payment_of_Min_Amount: (2)
Different and unique columns Payment_Behaviour: (6)
Different and unique columns Credit_Score: (3)
Different and unique columns SSN_Cleaned: (12500)
Different and unique columns Type_of_Loan_Cleaned: (508)

month_to_number = {
    'January': 1, 'February': 2, 'March': 3, 'April': 4, 'May': 5,
    'June': 6, 'July': 7, 'August': 8, 'September': 9, 'October': 10,
    'November': 11, 'December': 12
}

df_group['Month_Number'] = df_group['Month'].map(month_to_number)

Credit_Mix_to_Score = {
    'Good': 2,
    'Standard': 1,
    'Bad': 0
}

df_group['Credit_Mix_Score'] = df_group['Credit_Mix'].map(Credit_Mix_to_Score)

Payment_of_Min_Amount_to_bolean = {
    'Yes': 1,
    'No': 0
}

df_group['Payment_of_Min_Amount_Yes'] = df_group['Payment_of_Min_Amount'].map(Payment_of_Min_Amount_to_bolean)

Payment_Behaviour_to_Score = {
    'Low_spent_Small_value_payments': 1,
    'Low_spent_Medium_value_payments': 2,
    'Low_spent_Large_value_payments': 3,
    'High_spent_Small_value_payments': 4,
    'High_spent_Medium_value_payments': 5,
    'High_spent_Large_value_payments': 6
}

df_group['Payment_Behaviour_Score'] = df_group['Payment_Behaviour'].map(Payment_Behaviour_to_Score)

df_group['Credit_Score'].unique()

array(['Good', 'Standard', 'Poor', nan], dtype=object)

Credit_Score_to_Number = {
    'Good': 2,
    'Standard': 1,
    'Poor': 0
}

df_group['Credit_Score'] = df_group['Credit_Score'].map(Credit_Score_to_Number)

plt.figure(figsize=(17,10))
cor = df_group[numeric].corr(method="kendall")
sns.heatmap(cor, annot=True)
plt.title('Correlation of Numeric Features After Feature Engineering')
plt.show()

loan = df_group
loan['Type_of_Loan_Cleaned'] = loan['Type_of_Loan_Cleaned'].str.strip()
loan['Type_of_Loan_Cleaned'] = loan['Type_of_Loan_Cleaned'].str.replace(' ', '_', regex=True)
loan['Type_of_Loan_Cleaned'] = loan['Type_of_Loan_Cleaned'].str.replace('-', '_', regex=True)

loan = loan['Type_of_Loan_Cleaned'].str.get_dummies(sep=',_')
df_group = pd.concat([df_group, loan], axis=1)

df_group = pd.get_dummies(df_group, columns=['Occupation'])

df_group = df_group.select_dtypes(exclude=['object'])
df_group = df_group.drop(columns=['Month_Number'])
df_group

data_train = df_group[df_group['is_train'] == 0].drop(columns=['is_train'])
data_test = df_group[df_group['is_train'] == 1].drop(columns=['is_train'])

Credit_Score = data_train.pop('Credit_Score')
data_train['Credit_Score'] = Credit_Score

data_test = data_test.drop(columns=['Credit_Score'])

df_train.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 100000 entries, 0 to 99999
Data columns (total 29 columns):
 #   Column                    Non-Null Count   Dtype  
---  ------                    --------------   -----  
 0   ID                        100000 non-null  object 
 1   Customer_ID               100000 non-null  object 
 2   Month                     100000 non-null  object 
 3   Name                      90015 non-null   object 
 4   Age                       100000 non-null  object 
 5   SSN                       100000 non-null  object 
 6   Occupation                100000 non-null  object 
 7   Annual_Income             100000 non-null  object 
 8   Monthly_Inhand_Salary     84998 non-null   float64
 9   Num_Bank_Accounts         100000 non-null  int64  
 10  Num_Credit_Card           100000 non-null  int64  
 11  Interest_Rate             100000 non-null  int64  
 12  Num_of_Loan               100000 non-null  object 
 13  Type_of_Loan              88592 non-null   object 
 14  Delay_from_due_date       100000 non-null  int64  
 15  Num_of_Delayed_Payment    92998 non-null   object 
 16  Changed_Credit_Limit      100000 non-null  object 
 17  Num_Credit_Inquiries      98035 non-null   float64
 18  Credit_Mix                100000 non-null  object 
 19  Outstanding_Debt          100000 non-null  object 
 20  Credit_Utilization_Ratio  100000 non-null  float64
 21  Credit_History_Age        90970 non-null   object 
 22  Payment_of_Min_Amount     100000 non-null  object 
 23  Total_EMI_per_month       100000 non-null  float64
 24  Amount_invested_monthly   95521 non-null   object 
 25  Payment_Behaviour         100000 non-null  object 
 26  Monthly_Balance           98800 non-null   object 
 27  Credit_Score              100000 non-null  object 
 28  is_train                  100000 non-null  int64  
dtypes: float64(4), int64(5), object(20)
memory usage: 22.1+ MB

df_test.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 50000 entries, 0 to 49999
Data columns (total 28 columns):
 #   Column                    Non-Null Count  Dtype  
---  ------                    --------------  -----  
 0   ID                        50000 non-null  object 
 1   Customer_ID               50000 non-null  object 
 2   Month                     50000 non-null  object 
 3   Name                      44985 non-null  object 
 4   Age                       50000 non-null  object 
 5   SSN                       50000 non-null  object 
 6   Occupation                50000 non-null  object 
 7   Annual_Income             50000 non-null  object 
 8   Monthly_Inhand_Salary     42502 non-null  float64
 9   Num_Bank_Accounts         50000 non-null  int64  
 10  Num_Credit_Card           50000 non-null  int64  
 11  Interest_Rate             50000 non-null  int64  
 12  Num_of_Loan               50000 non-null  object 
 13  Type_of_Loan              44296 non-null  object 
 14  Delay_from_due_date       50000 non-null  int64  
 15  Num_of_Delayed_Payment    46502 non-null  object 
 16  Changed_Credit_Limit      50000 non-null  object 
 17  Num_Credit_Inquiries      48965 non-null  float64
 18  Credit_Mix                50000 non-null  object 
 19  Outstanding_Debt          50000 non-null  object 
 20  Credit_Utilization_Ratio  50000 non-null  float64
 21  Credit_History_Age        45530 non-null  object 
 22  Payment_of_Min_Amount     50000 non-null  object 
 23  Total_EMI_per_month       50000 non-null  float64
 24  Amount_invested_monthly   47729 non-null  object 
 25  Payment_Behaviour         50000 non-null  object 
 26  Monthly_Balance           49438 non-null  object 
 27  is_train                  50000 non-null  int64  
dtypes: float64(4), int64(5), object(19)
memory usage: 10.7+ MB

df_group.head()

from sklearn.preprocessing import LabelEncoder

categorical_columns = df_group.select_dtypes(include=['object', 'category']).columns

for col in categorical_columns:
    le = LabelEncoder()
    df_group[col] = le.fit_transform(df_group[col].astype(str))

print("Converted categorical features into numeric.")

Converted categorical features into numeric.

from imblearn.over_sampling import RandomOverSampler

X = df_train.drop(columns=['Credit_Score'])
y = df_train["Credit_Score"]

X, y = RandomOverSampler().fit_resample(X, y)

df_resampling = pd.concat([X, y], axis=1)
plt.figure(figsize=(8, 6))
sns.countplot(x='Credit_Score', data=df_resampling, palette='pastel')
plt.title('Distribution of Target Variable: Credit_Score After Resampling')
plt.xlabel('Credit_Score After Resampling')
plt.ylabel('Count')
plt.show()

<ipython-input-130-dc734f9f83f5>:3: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.

  sns.countplot(x='Credit_Score', data=df_resampling, palette='pastel')

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
print('X_train',X_train.shape)
print('y_train',y_train.shape)
print('X_test',X_test.shape)
print('y_test',y_test.shape)

X_train (127617, 28)
y_train (127617,)
X_test (31905, 28)
y_test (31905,)

from sklearn.preprocessing import LabelEncoder

le = LabelEncoder()
y_train = le.fit_transform(y_train)
y_test = le.transform(y_test)

df_group.fillna(0, inplace=True)

from sklearn.preprocessing import LabelEncoder
import pandas as pd

categorical_features = ['ID', 'Customer_ID', 'Month', 'Name', 'Age', 'SSN', 'Occupation', 'Annual_Income', 'Num_of_Loan', 'Type_of_Loan', 'Num_of_Delayed_Payment', 'Changed_Credit_Limit', 'Credit_Mix', 'Outstanding_Debt', 'Credit_History_Age', 'Payment_of_Min_Amount', 'Amount_invested_monthly', 'Payment_Behaviour', 'Monthly_Balance']

for feature in categorical_features:
    if feature in X_train.columns and feature in X_test.columns:
        le = LabelEncoder()

        le.fit(pd.concat([X_train[feature], X_test[feature]]).astype(str).unique())

        X_train[feature] = le.transform(X_train[feature].astype(str))
        X_test[feature] = le.transform(X_test[feature].astype(str))
    else:
        print(f"Feature '{feature}' not found in both X_train and X_test")

from xgboost import XGBClassifier
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd

xgb_model = XGBClassifier(use_label_encoder=False, eval_metric='mlogloss', random_state=42)

xgb_model.fit(X_train, y_train)

imp = pd.Series(data=xgb_model.feature_importances_, index=X_train.columns).sort_values(ascending=False)

plt.figure(figsize=(10, 12))
sns.barplot(y=imp.index, x=imp.values, palette="Blues_d", orient='h')
plt.title("Feature Importance", fontsize=16)
plt.xlabel("Importance Score", fontsize=14)
plt.ylabel("Features", fontsize=14)
plt.xticks(fontsize=12)
plt.yticks(fontsize=12)
plt.grid(axis='x', linestyle='--', alpha=0.7)
plt.tight_layout()
plt.show()

/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [07:43:17] WARNING: /workspace/src/learner.cc:740: 
Parameters: { "use_label_encoder" } are not used.

  warnings.warn(smsg, UserWarning)
<ipython-input-135-5b29f37426dc>:13: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.

  sns.barplot(y=imp.index, x=imp.values, palette="Blues_d", orient='h')

from sklearn.metrics import accuracy_score, classification_report, log_loss
from sklearn.metrics import f1_score, precision_score, recall_score, roc_auc_score

y_pred = xgb_model.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, average='weighted')
recall = recall_score(y_test, y_pred, average='weighted')

print("Accuracy:", accuracy)
print("Precision:", precision)
print("Recall:", recall)
print(classification_report(y_test, y_pred))
print("AUC-ROC Score:", roc_auc_score(y_test, xgb_model.predict_proba(X_test), multi_class='ovr', average='weighted'))

Accuracy: 0.820122237893747
Precision: 0.8192873295571445
Recall: 0.820122237893747
              precision    recall  f1-score   support

           0       0.82      0.93      0.87     10710
           1       0.83      0.86      0.84     10578
           2       0.81      0.67      0.73     10617

    accuracy                           0.82     31905
   macro avg       0.82      0.82      0.82     31905
weighted avg       0.82      0.82      0.82     31905

AUC-ROC Score: 0.9312729492479699

from sklearn.model_selection import GridSearchCV

param_grid = {
    'n_estimators': [50, 100, 200],
    'max_depth': [3, 5, 10],
    'learning_rate': [0.01, 0.1, 0.2],
    'subsample': [0.8, 1.0]
}

grid_search = GridSearchCV(XGBClassifier(use_label_encoder=False, eval_metric='mlogloss'),
                           param_grid, cv=5, scoring='accuracy', n_jobs=-1)
grid_search.fit(X_train, y_train)
print("Best Parameters XGBClassifier:", grid_search.best_params_)

/usr/local/lib/python3.10/dist-packages/joblib/externals/loky/process_executor.py:752: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.
  warnings.warn(
/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [08:41:17] WARNING: /workspace/src/learner.cc:740: 
Parameters: { "use_label_encoder" } are not used.

  warnings.warn(smsg, UserWarning)

Best Parameters XGBClassifier: {'learning_rate': 0.2, 'max_depth': 10, 'n_estimators': 200, 'subsample': 0.8}

best_params = grid_search.best_params_
optimized_xgb = XGBClassifier(
    use_label_encoder=False,
    eval_metric='mlogloss',
    n_estimators=best_params['n_estimators'],
    max_depth=best_params['max_depth'],
    learning_rate=best_params['learning_rate'],
    subsample=best_params['subsample']
)

eval_set = [(X_train, y_train), (X_test, y_test)]

optimized_xgb.fit(X_train, y_train, eval_set=eval_set, verbose=True)
results_optimized_xgb = optimized_xgb.evals_result()

epochs = len(results_optimized_xgb['validation_0']['mlogloss'])
x_axis = range(0, epochs)

plt.figure(figsize=(10, 6))
plt.plot(x_axis, results_optimized_xgb['validation_0']['mlogloss'], label='Train')
plt.plot(x_axis, results_optimized_xgb['validation_1']['mlogloss'], label='Validation')
plt.xlabel('Epochs')
plt.ylabel('Log Loss')
plt.title('Training and Validation Log Loss')
plt.legend()
plt.grid()
plt.show()

/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [08:48:40] WARNING: /workspace/src/learner.cc:740: 
Parameters: { "use_label_encoder" } are not used.

  warnings.warn(smsg, UserWarning)

[0]	validation_0-mlogloss:0.97079	validation_1-mlogloss:0.97546
[1]	validation_0-mlogloss:0.87821	validation_1-mlogloss:0.88724
[2]	validation_0-mlogloss:0.80828	validation_1-mlogloss:0.82175
[3]	validation_0-mlogloss:0.75284	validation_1-mlogloss:0.77045
[4]	validation_0-mlogloss:0.70945	validation_1-mlogloss:0.73094
[5]	validation_0-mlogloss:0.67420	validation_1-mlogloss:0.69953
[6]	validation_0-mlogloss:0.64561	validation_1-mlogloss:0.67437
[7]	validation_0-mlogloss:0.62000	validation_1-mlogloss:0.65264
[8]	validation_0-mlogloss:0.59681	validation_1-mlogloss:0.63383
[9]	validation_0-mlogloss:0.57665	validation_1-mlogloss:0.61815
[10]	validation_0-mlogloss:0.55835	validation_1-mlogloss:0.60361
[11]	validation_0-mlogloss:0.54008	validation_1-mlogloss:0.58900
[12]	validation_0-mlogloss:0.52510	validation_1-mlogloss:0.57715
[13]	validation_0-mlogloss:0.51202	validation_1-mlogloss:0.56692
[14]	validation_0-mlogloss:0.49934	validation_1-mlogloss:0.55724
[15]	validation_0-mlogloss:0.48750	validation_1-mlogloss:0.54842
[16]	validation_0-mlogloss:0.47507	validation_1-mlogloss:0.53940
[17]	validation_0-mlogloss:0.46492	validation_1-mlogloss:0.53249
[18]	validation_0-mlogloss:0.45438	validation_1-mlogloss:0.52438
[19]	validation_0-mlogloss:0.44542	validation_1-mlogloss:0.51793
[20]	validation_0-mlogloss:0.43685	validation_1-mlogloss:0.51154
[21]	validation_0-mlogloss:0.42769	validation_1-mlogloss:0.50463
[22]	validation_0-mlogloss:0.41912	validation_1-mlogloss:0.49758
[23]	validation_0-mlogloss:0.41174	validation_1-mlogloss:0.49241
[24]	validation_0-mlogloss:0.40445	validation_1-mlogloss:0.48685
[25]	validation_0-mlogloss:0.39623	validation_1-mlogloss:0.48070
[26]	validation_0-mlogloss:0.39003	validation_1-mlogloss:0.47576
[27]	validation_0-mlogloss:0.38500	validation_1-mlogloss:0.47176
[28]	validation_0-mlogloss:0.38015	validation_1-mlogloss:0.46804
[29]	validation_0-mlogloss:0.37395	validation_1-mlogloss:0.46347
[30]	validation_0-mlogloss:0.36709	validation_1-mlogloss:0.45814
[31]	validation_0-mlogloss:0.36088	validation_1-mlogloss:0.45349
[32]	validation_0-mlogloss:0.35700	validation_1-mlogloss:0.45057
[33]	validation_0-mlogloss:0.35281	validation_1-mlogloss:0.44739
[34]	validation_0-mlogloss:0.34783	validation_1-mlogloss:0.44391
[35]	validation_0-mlogloss:0.34205	validation_1-mlogloss:0.43932
[36]	validation_0-mlogloss:0.33694	validation_1-mlogloss:0.43576
[37]	validation_0-mlogloss:0.33233	validation_1-mlogloss:0.43261
[38]	validation_0-mlogloss:0.32824	validation_1-mlogloss:0.42983
[39]	validation_0-mlogloss:0.32345	validation_1-mlogloss:0.42682
[40]	validation_0-mlogloss:0.31951	validation_1-mlogloss:0.42355
[41]	validation_0-mlogloss:0.31562	validation_1-mlogloss:0.42101
[42]	validation_0-mlogloss:0.31141	validation_1-mlogloss:0.41789
[43]	validation_0-mlogloss:0.30756	validation_1-mlogloss:0.41520
[44]	validation_0-mlogloss:0.30377	validation_1-mlogloss:0.41232
[45]	validation_0-mlogloss:0.29928	validation_1-mlogloss:0.40887
[46]	validation_0-mlogloss:0.29614	validation_1-mlogloss:0.40702
[47]	validation_0-mlogloss:0.29114	validation_1-mlogloss:0.40337
[48]	validation_0-mlogloss:0.28680	validation_1-mlogloss:0.39978
[49]	validation_0-mlogloss:0.28319	validation_1-mlogloss:0.39753
[50]	validation_0-mlogloss:0.27714	validation_1-mlogloss:0.39267
[51]	validation_0-mlogloss:0.27181	validation_1-mlogloss:0.38874
[52]	validation_0-mlogloss:0.26847	validation_1-mlogloss:0.38657
[53]	validation_0-mlogloss:0.26530	validation_1-mlogloss:0.38434
[54]	validation_0-mlogloss:0.26186	validation_1-mlogloss:0.38180
[55]	validation_0-mlogloss:0.25868	validation_1-mlogloss:0.38006
[56]	validation_0-mlogloss:0.25597	validation_1-mlogloss:0.37822
[57]	validation_0-mlogloss:0.25279	validation_1-mlogloss:0.37646
[58]	validation_0-mlogloss:0.24998	validation_1-mlogloss:0.37440
[59]	validation_0-mlogloss:0.24674	validation_1-mlogloss:0.37217
[60]	validation_0-mlogloss:0.24340	validation_1-mlogloss:0.37004
[61]	validation_0-mlogloss:0.23985	validation_1-mlogloss:0.36786
[62]	validation_0-mlogloss:0.23592	validation_1-mlogloss:0.36534
[63]	validation_0-mlogloss:0.23195	validation_1-mlogloss:0.36259
[64]	validation_0-mlogloss:0.22845	validation_1-mlogloss:0.36053
[65]	validation_0-mlogloss:0.22536	validation_1-mlogloss:0.35843
[66]	validation_0-mlogloss:0.22218	validation_1-mlogloss:0.35683
[67]	validation_0-mlogloss:0.21900	validation_1-mlogloss:0.35462
[68]	validation_0-mlogloss:0.21575	validation_1-mlogloss:0.35273
[69]	validation_0-mlogloss:0.21299	validation_1-mlogloss:0.35084
[70]	validation_0-mlogloss:0.20951	validation_1-mlogloss:0.34865
[71]	validation_0-mlogloss:0.20664	validation_1-mlogloss:0.34659
[72]	validation_0-mlogloss:0.20359	validation_1-mlogloss:0.34475
[73]	validation_0-mlogloss:0.20142	validation_1-mlogloss:0.34325
[74]	validation_0-mlogloss:0.19907	validation_1-mlogloss:0.34166
[75]	validation_0-mlogloss:0.19664	validation_1-mlogloss:0.34010
[76]	validation_0-mlogloss:0.19361	validation_1-mlogloss:0.33818
[77]	validation_0-mlogloss:0.19128	validation_1-mlogloss:0.33634
[78]	validation_0-mlogloss:0.18866	validation_1-mlogloss:0.33474
[79]	validation_0-mlogloss:0.18685	validation_1-mlogloss:0.33360
[80]	validation_0-mlogloss:0.18461	validation_1-mlogloss:0.33239
[81]	validation_0-mlogloss:0.18244	validation_1-mlogloss:0.33113
[82]	validation_0-mlogloss:0.18038	validation_1-mlogloss:0.32992
[83]	validation_0-mlogloss:0.17756	validation_1-mlogloss:0.32806
[84]	validation_0-mlogloss:0.17537	validation_1-mlogloss:0.32689
[85]	validation_0-mlogloss:0.17322	validation_1-mlogloss:0.32594
[86]	validation_0-mlogloss:0.17051	validation_1-mlogloss:0.32438
[87]	validation_0-mlogloss:0.16720	validation_1-mlogloss:0.32254
[88]	validation_0-mlogloss:0.16500	validation_1-mlogloss:0.32124
[89]	validation_0-mlogloss:0.16290	validation_1-mlogloss:0.32031
[90]	validation_0-mlogloss:0.16044	validation_1-mlogloss:0.31882
[91]	validation_0-mlogloss:0.15836	validation_1-mlogloss:0.31762
[92]	validation_0-mlogloss:0.15641	validation_1-mlogloss:0.31644
[93]	validation_0-mlogloss:0.15438	validation_1-mlogloss:0.31503
[94]	validation_0-mlogloss:0.15279	validation_1-mlogloss:0.31429
[95]	validation_0-mlogloss:0.15086	validation_1-mlogloss:0.31297
[96]	validation_0-mlogloss:0.14914	validation_1-mlogloss:0.31206
[97]	validation_0-mlogloss:0.14759	validation_1-mlogloss:0.31110
[98]	validation_0-mlogloss:0.14576	validation_1-mlogloss:0.30998
[99]	validation_0-mlogloss:0.14350	validation_1-mlogloss:0.30863
[100]	validation_0-mlogloss:0.14180	validation_1-mlogloss:0.30766
[101]	validation_0-mlogloss:0.13957	validation_1-mlogloss:0.30629
[102]	validation_0-mlogloss:0.13779	validation_1-mlogloss:0.30554
[103]	validation_0-mlogloss:0.13619	validation_1-mlogloss:0.30477
[104]	validation_0-mlogloss:0.13426	validation_1-mlogloss:0.30354
[105]	validation_0-mlogloss:0.13306	validation_1-mlogloss:0.30296
[106]	validation_0-mlogloss:0.13139	validation_1-mlogloss:0.30212
[107]	validation_0-mlogloss:0.12989	validation_1-mlogloss:0.30151
[108]	validation_0-mlogloss:0.12812	validation_1-mlogloss:0.30063
[109]	validation_0-mlogloss:0.12649	validation_1-mlogloss:0.29984
[110]	validation_0-mlogloss:0.12508	validation_1-mlogloss:0.29911
[111]	validation_0-mlogloss:0.12324	validation_1-mlogloss:0.29799
[112]	validation_0-mlogloss:0.12197	validation_1-mlogloss:0.29728
[113]	validation_0-mlogloss:0.12030	validation_1-mlogloss:0.29640
[114]	validation_0-mlogloss:0.11892	validation_1-mlogloss:0.29571
[115]	validation_0-mlogloss:0.11681	validation_1-mlogloss:0.29453
[116]	validation_0-mlogloss:0.11585	validation_1-mlogloss:0.29404
[117]	validation_0-mlogloss:0.11434	validation_1-mlogloss:0.29308
[118]	validation_0-mlogloss:0.11305	validation_1-mlogloss:0.29229
[119]	validation_0-mlogloss:0.11171	validation_1-mlogloss:0.29146
[120]	validation_0-mlogloss:0.11063	validation_1-mlogloss:0.29114
[121]	validation_0-mlogloss:0.10906	validation_1-mlogloss:0.29021
[122]	validation_0-mlogloss:0.10765	validation_1-mlogloss:0.28949
[123]	validation_0-mlogloss:0.10672	validation_1-mlogloss:0.28906
[124]	validation_0-mlogloss:0.10530	validation_1-mlogloss:0.28821
[125]	validation_0-mlogloss:0.10427	validation_1-mlogloss:0.28755
[126]	validation_0-mlogloss:0.10327	validation_1-mlogloss:0.28687
[127]	validation_0-mlogloss:0.10225	validation_1-mlogloss:0.28639
[128]	validation_0-mlogloss:0.10118	validation_1-mlogloss:0.28594
[129]	validation_0-mlogloss:0.10000	validation_1-mlogloss:0.28526
[130]	validation_0-mlogloss:0.09897	validation_1-mlogloss:0.28481
[131]	validation_0-mlogloss:0.09791	validation_1-mlogloss:0.28409
[132]	validation_0-mlogloss:0.09667	validation_1-mlogloss:0.28360
[133]	validation_0-mlogloss:0.09532	validation_1-mlogloss:0.28283
[134]	validation_0-mlogloss:0.09409	validation_1-mlogloss:0.28228
[135]	validation_0-mlogloss:0.09288	validation_1-mlogloss:0.28156
[136]	validation_0-mlogloss:0.09151	validation_1-mlogloss:0.28087
[137]	validation_0-mlogloss:0.09033	validation_1-mlogloss:0.28018
[138]	validation_0-mlogloss:0.08961	validation_1-mlogloss:0.27995
[139]	validation_0-mlogloss:0.08834	validation_1-mlogloss:0.27933
[140]	validation_0-mlogloss:0.08725	validation_1-mlogloss:0.27897
[141]	validation_0-mlogloss:0.08628	validation_1-mlogloss:0.27866
[142]	validation_0-mlogloss:0.08537	validation_1-mlogloss:0.27822
[143]	validation_0-mlogloss:0.08439	validation_1-mlogloss:0.27770
[144]	validation_0-mlogloss:0.08361	validation_1-mlogloss:0.27737
[145]	validation_0-mlogloss:0.08232	validation_1-mlogloss:0.27670
[146]	validation_0-mlogloss:0.08134	validation_1-mlogloss:0.27616
[147]	validation_0-mlogloss:0.08038	validation_1-mlogloss:0.27574
[148]	validation_0-mlogloss:0.07939	validation_1-mlogloss:0.27530
[149]	validation_0-mlogloss:0.07870	validation_1-mlogloss:0.27496
[150]	validation_0-mlogloss:0.07797	validation_1-mlogloss:0.27459
[151]	validation_0-mlogloss:0.07692	validation_1-mlogloss:0.27394
[152]	validation_0-mlogloss:0.07626	validation_1-mlogloss:0.27359
[153]	validation_0-mlogloss:0.07525	validation_1-mlogloss:0.27310
[154]	validation_0-mlogloss:0.07438	validation_1-mlogloss:0.27277
[155]	validation_0-mlogloss:0.07351	validation_1-mlogloss:0.27242
[156]	validation_0-mlogloss:0.07256	validation_1-mlogloss:0.27217
[157]	validation_0-mlogloss:0.07178	validation_1-mlogloss:0.27208
[158]	validation_0-mlogloss:0.07114	validation_1-mlogloss:0.27180
[159]	validation_0-mlogloss:0.07040	validation_1-mlogloss:0.27144
[160]	validation_0-mlogloss:0.06968	validation_1-mlogloss:0.27107
[161]	validation_0-mlogloss:0.06887	validation_1-mlogloss:0.27071
[162]	validation_0-mlogloss:0.06806	validation_1-mlogloss:0.27028
[163]	validation_0-mlogloss:0.06735	validation_1-mlogloss:0.27003
[164]	validation_0-mlogloss:0.06671	validation_1-mlogloss:0.26983
[165]	validation_0-mlogloss:0.06591	validation_1-mlogloss:0.26958
[166]	validation_0-mlogloss:0.06515	validation_1-mlogloss:0.26920
[167]	validation_0-mlogloss:0.06438	validation_1-mlogloss:0.26877
[168]	validation_0-mlogloss:0.06357	validation_1-mlogloss:0.26827
[169]	validation_0-mlogloss:0.06296	validation_1-mlogloss:0.26800
[170]	validation_0-mlogloss:0.06228	validation_1-mlogloss:0.26777
[171]	validation_0-mlogloss:0.06144	validation_1-mlogloss:0.26739
[172]	validation_0-mlogloss:0.06064	validation_1-mlogloss:0.26718
[173]	validation_0-mlogloss:0.05989	validation_1-mlogloss:0.26689
[174]	validation_0-mlogloss:0.05919	validation_1-mlogloss:0.26666
[175]	validation_0-mlogloss:0.05878	validation_1-mlogloss:0.26650
[176]	validation_0-mlogloss:0.05841	validation_1-mlogloss:0.26634
[177]	validation_0-mlogloss:0.05772	validation_1-mlogloss:0.26617
[178]	validation_0-mlogloss:0.05711	validation_1-mlogloss:0.26595
[179]	validation_0-mlogloss:0.05650	validation_1-mlogloss:0.26573
[180]	validation_0-mlogloss:0.05573	validation_1-mlogloss:0.26535
[181]	validation_0-mlogloss:0.05518	validation_1-mlogloss:0.26528
[182]	validation_0-mlogloss:0.05445	validation_1-mlogloss:0.26492
[183]	validation_0-mlogloss:0.05402	validation_1-mlogloss:0.26466
[184]	validation_0-mlogloss:0.05344	validation_1-mlogloss:0.26446
[185]	validation_0-mlogloss:0.05300	validation_1-mlogloss:0.26431
[186]	validation_0-mlogloss:0.05247	validation_1-mlogloss:0.26408
[187]	validation_0-mlogloss:0.05187	validation_1-mlogloss:0.26385
[188]	validation_0-mlogloss:0.05123	validation_1-mlogloss:0.26374
[189]	validation_0-mlogloss:0.05050	validation_1-mlogloss:0.26346
[190]	validation_0-mlogloss:0.05006	validation_1-mlogloss:0.26333
[191]	validation_0-mlogloss:0.04953	validation_1-mlogloss:0.26312
[192]	validation_0-mlogloss:0.04883	validation_1-mlogloss:0.26300
[193]	validation_0-mlogloss:0.04837	validation_1-mlogloss:0.26288
[194]	validation_0-mlogloss:0.04782	validation_1-mlogloss:0.26273
[195]	validation_0-mlogloss:0.04726	validation_1-mlogloss:0.26244
[196]	validation_0-mlogloss:0.04678	validation_1-mlogloss:0.26216
[197]	validation_0-mlogloss:0.04632	validation_1-mlogloss:0.26201
[198]	validation_0-mlogloss:0.04577	validation_1-mlogloss:0.26198
[199]	validation_0-mlogloss:0.04538	validation_1-mlogloss:0.26183

from sklearn.preprocessing import LabelEncoder
import pandas as pd

# Assuming categorical_features contains your list of object type columns
categorical_features = ['ID', 'Customer_ID', 'Month', 'Name', 'Age', 'SSN', 'Occupation', 'Annual_Income', 'Num_of_Loan', 'Type_of_Loan', 'Num_of_Delayed_Payment', 'Changed_Credit_Limit', 'Credit_Mix', 'Outstanding_Debt', 'Credit_History_Age', 'Payment_of_Min_Amount', 'Amount_invested_monthly', 'Payment_Behaviour', 'Monthly_Balance']

for feature in categorical_features:
    if feature in X_train.columns and feature in X_test.columns:
        le = LabelEncoder()
        # Fit on the combined unique values from both train and test
        le.fit(pd.concat([X_train[feature], X_test[feature]]).astype(str).unique())
        X_train[feature] = le.transform(X_train[feature].astype(str))
        X_test[feature] = le.transform(X_test[feature].astype(str))
    else:
        print(f"Feature '{feature}' not found in both X_train and X_test")

<ipython-input-142-7d25b58549fd>:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-142-7d25b58549fd>:13: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test[feature] = le.transform(X_test[feature].astype(str))
<ipython-input-142-7d25b58549fd>:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-142-7d25b58549fd>:13: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test[feature] = le.transform(X_test[feature].astype(str))
<ipython-input-142-7d25b58549fd>:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-142-7d25b58549fd>:13: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test[feature] = le.transform(X_test[feature].astype(str))
<ipython-input-142-7d25b58549fd>:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-142-7d25b58549fd>:13: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test[feature] = le.transform(X_test[feature].astype(str))
<ipython-input-142-7d25b58549fd>:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-142-7d25b58549fd>:13: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test[feature] = le.transform(X_test[feature].astype(str))
<ipython-input-142-7d25b58549fd>:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-142-7d25b58549fd>:13: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test[feature] = le.transform(X_test[feature].astype(str))
<ipython-input-142-7d25b58549fd>:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-142-7d25b58549fd>:13: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test[feature] = le.transform(X_test[feature].astype(str))
<ipython-input-142-7d25b58549fd>:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-142-7d25b58549fd>:13: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test[feature] = le.transform(X_test[feature].astype(str))
<ipython-input-142-7d25b58549fd>:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-142-7d25b58549fd>:13: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test[feature] = le.transform(X_test[feature].astype(str))
<ipython-input-142-7d25b58549fd>:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-142-7d25b58549fd>:13: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test[feature] = le.transform(X_test[feature].astype(str))
<ipython-input-142-7d25b58549fd>:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-142-7d25b58549fd>:13: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test[feature] = le.transform(X_test[feature].astype(str))
<ipython-input-142-7d25b58549fd>:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-142-7d25b58549fd>:13: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test[feature] = le.transform(X_test[feature].astype(str))
<ipython-input-142-7d25b58549fd>:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-142-7d25b58549fd>:13: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test[feature] = le.transform(X_test[feature].astype(str))
<ipython-input-142-7d25b58549fd>:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-142-7d25b58549fd>:13: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test[feature] = le.transform(X_test[feature].astype(str))
<ipython-input-142-7d25b58549fd>:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-142-7d25b58549fd>:13: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test[feature] = le.transform(X_test[feature].astype(str))
<ipython-input-142-7d25b58549fd>:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-142-7d25b58549fd>:13: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test[feature] = le.transform(X_test[feature].astype(str))
<ipython-input-142-7d25b58549fd>:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-142-7d25b58549fd>:13: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test[feature] = le.transform(X_test[feature].astype(str))
<ipython-input-142-7d25b58549fd>:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-142-7d25b58549fd>:13: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test[feature] = le.transform(X_test[feature].astype(str))
<ipython-input-142-7d25b58549fd>:12: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-142-7d25b58549fd>:13: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test[feature] = le.transform(X_test[feature].astype(str))

from sklearn.preprocessing import LabelEncoder
import pandas as pd

# Assuming categorical_features contains your list of object type columns
categorical_features = ['ID', 'Customer_ID', 'Month', 'Name', 'Age', 'SSN', 'Occupation', 'Annual_Income', 'Num_of_Loan', 'Type_of_Loan', 'Num_of_Delayed_Payment', 'Changed_Credit_Limit', 'Credit_Mix', 'Outstanding_Debt', 'Credit_History_Age', 'Payment_of_Min_Amount', 'Amount_invested_monthly', 'Payment_Behaviour', 'Monthly_Balance']

# Ensure X_test has the same columns as X_train used during training
X_test = X_test[[col for col in X_train.columns if col in X_test.columns]]

for feature in categorical_features:
    if feature in X_train.columns and feature in X_test.columns:
        le = LabelEncoder()
        # Fit on the combined unique values from both train and test
        le.fit(pd.concat([X_train[feature], X_test[feature]]).astype(str).unique())
        X_train[feature] = le.transform(X_train[feature].astype(str))
        X_test[feature] = le.transform(X_test[feature].astype(str))
    else:
        print(f"Feature '{feature}' not found in both X_train and X_test")

<ipython-input-152-65e1e9859ddb>:15: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-152-65e1e9859ddb>:15: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-152-65e1e9859ddb>:15: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-152-65e1e9859ddb>:15: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-152-65e1e9859ddb>:15: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-152-65e1e9859ddb>:15: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-152-65e1e9859ddb>:15: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-152-65e1e9859ddb>:15: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-152-65e1e9859ddb>:15: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-152-65e1e9859ddb>:15: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-152-65e1e9859ddb>:15: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-152-65e1e9859ddb>:15: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-152-65e1e9859ddb>:15: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-152-65e1e9859ddb>:15: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-152-65e1e9859ddb>:15: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-152-65e1e9859ddb>:15: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-152-65e1e9859ddb>:15: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-152-65e1e9859ddb>:15: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-152-65e1e9859ddb>:15: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))

y_pred = optimized_xgb.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, average='weighted')
recall = recall_score(y_test, y_pred, average='weighted')

print("Accuracy:", accuracy)
print("Precision:", precision)
print("Recall:", recall)
print(classification_report(y_test, y_pred))
print("AUC-ROC Score:", roc_auc_score(y_test, optimized_xgb.predict_proba(X_test), multi_class='ovr', average='weighted'))

Accuracy: 0.6871114299378288
Precision: 0.6917502759377729
Recall: 0.6871114299378288
              precision    recall  f1-score   support

           0       0.62      0.74      0.67      1465
           1       0.64      0.65      0.65      2488
           2       0.74      0.69      0.72      4411

    accuracy                           0.69      8364
   macro avg       0.67      0.69      0.68      8364
weighted avg       0.69      0.69      0.69      8364

AUC-ROC Score: 0.8248599898132657

from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import LabelEncoder

X_Kfold = X_train
y_Kfold = y_train

optimized_xgb_kfold = optimized_xgb

# K-Fold Cross Validation
kf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)

# Manual K-Fold
f1_scores, precision_scores, recall_scores = [], [], []

# --- Fit LabelEncoders outside the loop ---
categorical_features = X.select_dtypes(include=['object']).columns
label_encoders = {}  # Store LabelEncoders for each feature

for feature in categorical_features:
    label_encoders[feature] = LabelEncoder()
    # Fit on all data to avoid unseen labels
    label_encoders[feature].fit(X[feature].astype(str).unique())

# --- Now iterate through folds ---
for train_index, test_index in kf.split(X_Kfold, y_Kfold):
    # Select data using .iloc
    X_train_fold = X.iloc[train_index]
    X_test_fold = X.iloc[test_index]
    y_train_fold = y.iloc[train_index]
    y_test_fold = y.iloc[test_index]

    # --- Apply Label Encoding using pre-fitted encoders ---
    for feature in categorical_features:
        X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
        X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))

    # --- Convert all data to float after encoding ---
    X_train_fold = X_train_fold.astype(float)
    X_test_fold = X_test_fold.astype(float)

    # Encode the target variable within the loop
    le = LabelEncoder()
    y_train_fold = le.fit_transform(y_train_fold)
    y_test_fold = le.transform(y_test_fold)

    optimized_xgb_kfold.fit(X_train_fold, y_train_fold)  # Use the converted data
    y_pred = optimized_xgb_kfold.predict(X_test_fold)  # Use the converted data

    f1_scores.append(f1_score(y_test_fold, y_pred, average='weighted'))
    precision_scores.append(precision_score(y_test_fold, y_pred, average='weighted'))
    recall_scores.append(recall_score(y_test_fold, y_pred, average='weighted'))

print("Mean F1-Score:", np.mean(f1_scores))
print("Mean Precision:", np.mean(precision_scores))

<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [09:01:03] WARNING: /workspace/src/learner.cc:740: 
Parameters: { "use_label_encoder" } are not used.

  warnings.warn(smsg, UserWarning)
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [09:01:25] WARNING: /workspace/src/learner.cc:740: 
Parameters: { "use_label_encoder" } are not used.

  warnings.warn(smsg, UserWarning)
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [09:01:48] WARNING: /workspace/src/learner.cc:740: 
Parameters: { "use_label_encoder" } are not used.

  warnings.warn(smsg, UserWarning)
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [09:02:05] WARNING: /workspace/src/learner.cc:740: 
Parameters: { "use_label_encoder" } are not used.

  warnings.warn(smsg, UserWarning)
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:34: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train_fold[feature] = label_encoders[feature].transform(X_train_fold[feature].astype(str))
<ipython-input-157-30743ffd0d62>:35: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_test_fold[feature] = label_encoders[feature].transform(X_test_fold[feature].astype(str))
/usr/local/lib/python3.10/dist-packages/xgboost/core.py:158: UserWarning: [09:02:22] WARNING: /workspace/src/learner.cc:740: 
Parameters: { "use_label_encoder" } are not used.

  warnings.warn(smsg, UserWarning)

Mean F1-Score: 0.795492429540106
Mean Precision: 0.795518354123495

from sklearn.preprocessing import LabelEncoder
import pandas as pd

# Assuming categorical_features contains your list of object type columns
categorical_features = ['ID', 'Customer_ID', 'Month', 'Name', 'Age', 'SSN', 'Occupation', 'Annual_Income', 'Num_of_Loan', 'Type_of_Loan', 'Num_of_Delayed_Payment', 'Changed_Credit_Limit', 'Credit_Mix', 'Outstanding_Debt', 'Credit_History_Age', 'Payment_of_Min_Amount', 'Amount_invested_monthly', 'Payment_Behaviour', 'Monthly_Balance']

# Ensure X_test has the same columns as X_train used during training
# This line is crucial to ensure compatibility
X_test = X_test[[col for col in X_train.columns if col in X_test.columns]]

for feature in categorical_features:
    if feature in X_train.columns and feature in X_test.columns:
        le = LabelEncoder()
        # Fit on the combined unique values from both train and test
        le.fit(pd.concat([X_train[feature], X_test[feature]]).astype(str).unique())
        X_train[feature] = le.transform(X_train[feature].astype(str))
        X_test[feature] = le.transform(X_test[feature].astype(str))
    else:
        print(f"Feature '{feature}' not found in both X_train and X_test")

# --- Convert all data to float after encoding ---
# This line ensures XGBoost compatibility
X_test = X_test.astype(float)

<ipython-input-159-379acd4c14c7>:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-159-379acd4c14c7>:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-159-379acd4c14c7>:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-159-379acd4c14c7>:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-159-379acd4c14c7>:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-159-379acd4c14c7>:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-159-379acd4c14c7>:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-159-379acd4c14c7>:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-159-379acd4c14c7>:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-159-379acd4c14c7>:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-159-379acd4c14c7>:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-159-379acd4c14c7>:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-159-379acd4c14c7>:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-159-379acd4c14c7>:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-159-379acd4c14c7>:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-159-379acd4c14c7>:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-159-379acd4c14c7>:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-159-379acd4c14c7>:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))
<ipython-input-159-379acd4c14c7>:16: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  X_train[feature] = le.transform(X_train[feature].astype(str))

from sklearn.ensemble import RandomForestClassifier

rf_model = RandomForestClassifier(random_state=42)
rf_model.fit(X_train, y_train)

# Plot features importances
imp = pd.Series(data=rf_model.feature_importances_, index=X_train.columns).sort_values(ascending=False)
plt.figure(figsize=(10,12))
plt.title("Feature importance")
ax = sns.barplot(y=imp.index, x=imp.values, palette="Blues_d", orient='h')

<ipython-input-160-7a7af09c5d56>:10: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.

  ax = sns.barplot(y=imp.index, x=imp.values, palette="Blues_d", orient='h')

from sklearn.ensemble import RandomForestClassifier

train_scores = []
val_scores = []

for n_estimators in range(10, 201, 10):
    rf_model = RandomForestClassifier(n_estimators=n_estimators, random_state=42)
    rf_model.fit(X_train, y_train)


    train_pred = rf_model.predict(X_train)
    train_acc = accuracy_score(y_train, train_pred)
    train_scores.append(train_acc)

    val_pred = rf_model.predict(X_test)
    val_acc = accuracy_score(y_test, val_pred)
    val_scores.append(val_acc)

plt.figure(figsize=(10, 6))
plt.plot(range(10, 201, 10), train_scores, label='Train Accuracy')
plt.plot(range(10, 201, 10), val_scores, label='Validation Accuracy')
plt.xlabel('Number of Trees (n_estimators)')
plt.ylabel('Accuracy')
plt.title('Random Forest Training vs Validation Accuracy')
plt.legend()
plt.grid()
plt.show()

final_rf_model = RandomForestClassifier(n_estimators=100, random_state=42)
final_rf_model.fit(X_train, y_train)
y_pred = final_rf_model.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, average='weighted')
recall = recall_score(y_test, y_pred, average='weighted')

print("Accuracy:", accuracy)
print("Precision:", precision)
print("Recall:", recall)
print(classification_report(y_test, y_pred))
print("AUC-ROC Score:", roc_auc_score(y_test, final_rf_model.predict_proba(X_test), multi_class='ovr', average='weighted'))

Accuracy: 0.7882229861007324
Precision: 0.7883267561389053
Recall: 0.7882229861007324
              precision    recall  f1-score   support

           0       0.73      0.71      0.72      1161
           1       0.78      0.81      0.80      1908
           2       0.81      0.80      0.81      3622

    accuracy                           0.79      6691
   macro avg       0.77      0.78      0.77      6691
weighted avg       0.79      0.79      0.79      6691

AUC-ROC Score: 0.8948954966339161

import lightgbm as lgb

lgbm = lgb.LGBMClassifier(objective='multiclass', random_state=42)
lgbm.fit(X_train, y_train)

# Plot features importances
imp = pd.Series(data=rf_model.feature_importances_, index=X_train.columns).sort_values(ascending=False)
plt.figure(figsize=(10,12))
plt.title("Feature importance")
ax = sns.barplot(y=imp.index, x=imp.values, palette="Blues_d", orient='h')

/usr/local/lib/python3.10/dist-packages/dask/dataframe/__init__.py:42: FutureWarning: 
Dask dataframe query planning is disabled because dask-expr is not installed.

You can install it with `pip install dask[dataframe]` or `conda install dask`.
This will raise in a future version.

  warnings.warn(msg, FutureWarning)

[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.003266 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 5084
[LightGBM] [Info] Number of data points in the train set: 26761, number of used features: 27
[LightGBM] [Info] Start training from score -1.714587
[LightGBM] [Info] Start training from score -1.222110
[LightGBM] [Info] Start training from score -0.643683

<ipython-input-163-ba426aead1c0>:10: FutureWarning: 

Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.

  ax = sns.barplot(y=imp.index, x=imp.values, palette="Blues_d", orient='h')

lgbm.fit(X_train, y_train, eval_metric='multi_logloss', eval_set=eval_set)
results_lgbm = lgbm.evals_result_

epochs = len(results_lgbm['valid_0']['multi_logloss'])
x_axis = range(0, epochs)

plt.figure(figsize=(10, 6))
plt.plot(x_axis, results_lgbm['valid_0']['multi_logloss'], label='Train')
plt.plot(x_axis, results_lgbm['valid_1']['multi_logloss'], label='Validation')
plt.xlabel('Epochs')
plt.ylabel('Log Loss')
plt.title('Training and Validation Log Loss')
plt.legend()
plt.grid()
plt.show()

[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.002209 seconds.
You can set `force_row_wise=true` to remove the overhead.
And if memory is not enough, you can set `force_col_wise=true`.
[LightGBM] [Info] Total Bins 5084
[LightGBM] [Info] Number of data points in the train set: 26761, number of used features: 27
[LightGBM] [Info] Start training from score -1.714587
[LightGBM] [Info] Start training from score -1.222110
[LightGBM] [Info] Start training from score -0.643683

y_pred = lgbm.predict(X_test)

accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, average='weighted')
recall = recall_score(y_test, y_pred, average='weighted')

print("Accuracy:", accuracy)
print("Precision:", precision)
print("Recall:", recall)
print(classification_report(y_test, y_pred))
print("AUC-ROC Score:", roc_auc_score(y_test, lgbm.predict_proba(X_test), multi_class='ovr', average='weighted'))

Accuracy: 0.7587804513525631
Precision: 0.7603072631063886
Recall: 0.7587804513525631
              precision    recall  f1-score   support

           0       0.68      0.73      0.70      1161
           1       0.75      0.75      0.75      1908
           2       0.79      0.77      0.78      3622

    accuracy                           0.76      6691
   macro avg       0.74      0.75      0.74      6691
weighted avg       0.76      0.76      0.76      6691

AUC-ROC Score: 0.8787710274426586

# @title
# prompt: target variable is credit_score make a deep learning model using pytoarch add more layers cause the model is underfitting the loss is nan i need good accuracy

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import TensorDataset, DataLoader

# Define the neural network architecture with more layers
class CreditScoreClassifier(nn.Module):
    def __init__(self, input_size):
        super(CreditScoreClassifier, self).__init__()
        self.fc1 = nn.Linear(input_size, 256)  # Increased neurons
        self.relu1 = nn.ReLU()
        self.dropout1 = nn.Dropout(0.3) # Add dropout for regularization
        self.fc2 = nn.Linear(256, 128)  # Added another layer
        self.relu2 = nn.ReLU()
        self.dropout2 = nn.Dropout(0.3) # Add dropout
        self.fc3 = nn.Linear(128, 64) # Added another layer
        self.relu3 = nn.ReLU()
        self.fc4 = nn.Linear(64, 3) # Output layer (3 classes)

    def forward(self, x):
        x = self.fc1(x)
        x = self.relu1(x)
        x = self.dropout1(x)
        x = self.fc2(x)
        x = self.relu2(x)
        x = self.dropout2(x)
        x = self.fc3(x)
        x = self.relu3(x)
        x = self.fc4(x)
        return x

# Convert data to PyTorch tensors
X_train_tensor = torch.tensor(X_train.values, dtype=torch.float32)
y_train_tensor = torch.tensor(y_train, dtype=torch.long)  # Use long for class labels
X_test_tensor = torch.tensor(X_test.values, dtype=torch.float32)
y_test_tensor = torch.tensor(y_test, dtype=torch.long)

# Create datasets and dataloaders
train_dataset = TensorDataset(X_train_tensor, y_train_tensor)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)  # Adjust batch size

test_dataset = TensorDataset(X_test_tensor, y_test_tensor)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)

# Initialize the model, loss function, and optimizer
input_size = X_train.shape[1]
model = CreditScoreClassifier(input_size)
criterion = nn.CrossEntropyLoss()  # Use CrossEntropyLoss for multi-class classification
optimizer = optim.Adam(model.parameters(), lr=0.001) # Use Adam optimizer

# Training loop with more epochs
num_epochs = 50  # Increase the number of epochs
for epoch in range(num_epochs):
    for i, (inputs, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        if (i + 1) % 100 == 0:
            print(f'Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{len(train_loader)}], Loss: {loss.item():.4f}')

# Evaluation
model.eval()
correct = 0
total = 0
with torch.no_grad():
    for inputs, labels in test_loader:
        outputs = model(inputs)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

accuracy = 100 * correct / total
print(f'Test Accuracy: {accuracy:.2f}%')

Epoch [1/100], Loss: nan
Epoch [2/100], Loss: nan
Epoch [3/100], Loss: nan
Epoch [4/100], Loss: nan
Epoch [5/100], Loss: nan
Epoch [6/100], Loss: nan
Epoch [7/100], Loss: nan
Epoch [8/100], Loss: nan
Epoch [9/100], Loss: nan
Epoch [10/100], Loss: nan
Epoch [11/100], Loss: nan
Epoch [12/100], Loss: nan
Epoch [13/100], Loss: nan
Epoch [14/100], Loss: nan
Epoch [15/100], Loss: nan
Epoch [16/100], Loss: nan
Epoch [17/100], Loss: nan
Epoch [18/100], Loss: nan
Epoch [19/100], Loss: nan
Epoch [20/100], Loss: nan
Epoch [21/100], Loss: nan
Epoch [22/100], Loss: nan
Epoch [23/100], Loss: nan
Epoch [24/100], Loss: nan
Epoch [25/100], Loss: nan
Epoch [26/100], Loss: nan
Epoch [27/100], Loss: nan
Epoch [28/100], Loss: nan
Epoch [29/100], Loss: nan
Epoch [30/100], Loss: nan
Epoch [31/100], Loss: nan
Epoch [32/100], Loss: nan
Epoch [33/100], Loss: nan
Epoch [34/100], Loss: nan
Epoch [35/100], Loss: nan
Epoch [36/100], Loss: nan
Epoch [37/100], Loss: nan
Epoch [38/100], Loss: nan
Epoch [39/100], Loss: nan
Epoch [40/100], Loss: nan
Epoch [41/100], Loss: nan
Epoch [42/100], Loss: nan
Epoch [43/100], Loss: nan
Epoch [44/100], Loss: nan
Epoch [45/100], Loss: nan
Epoch [46/100], Loss: nan
Epoch [47/100], Loss: nan
Epoch [48/100], Loss: nan
Epoch [49/100], Loss: nan
Epoch [50/100], Loss: nan
Epoch [51/100], Loss: nan
Epoch [52/100], Loss: nan
Epoch [53/100], Loss: nan
Epoch [54/100], Loss: nan
Epoch [55/100], Loss: nan
Epoch [56/100], Loss: nan
Epoch [57/100], Loss: nan
Epoch [58/100], Loss: nan
Epoch [59/100], Loss: nan
Epoch [60/100], Loss: nan
Epoch [61/100], Loss: nan
Epoch [62/100], Loss: nan
Epoch [63/100], Loss: nan
Epoch [64/100], Loss: nan
Epoch [65/100], Loss: nan
Epoch [66/100], Loss: nan
Epoch [67/100], Loss: nan
Epoch [68/100], Loss: nan
Epoch [69/100], Loss: nan
Epoch [70/100], Loss: nan
Epoch [71/100], Loss: nan
Epoch [72/100], Loss: nan
Epoch [73/100], Loss: nan
Epoch [74/100], Loss: nan
Epoch [75/100], Loss: nan
Epoch [76/100], Loss: nan
Epoch [77/100], Loss: nan
Epoch [78/100], Loss: nan
Epoch [79/100], Loss: nan
Epoch [80/100], Loss: nan
Epoch [81/100], Loss: nan
Epoch [82/100], Loss: nan
Epoch [83/100], Loss: nan
Epoch [84/100], Loss: nan
Epoch [85/100], Loss: nan
Epoch [86/100], Loss: nan
Epoch [87/100], Loss: nan
Epoch [88/100], Loss: nan
Epoch [89/100], Loss: nan
Epoch [90/100], Loss: nan
Epoch [91/100], Loss: nan
Epoch [92/100], Loss: nan
Epoch [93/100], Loss: nan
Epoch [94/100], Loss: nan
Epoch [95/100], Loss: nan
Epoch [96/100], Loss: nan
Epoch [97/100], Loss: nan
Epoch [98/100], Loss: nan
Epoch [99/100], Loss: nan
Epoch [100/100], Loss: nan
Accuracy of the model on the test images: 17.35166641757585%

# @title
# Get the feature names used during training
training_features = optimized_xgb.get_booster().feature_names

# Ensure training_features are present in data_test
# Instead of filtering, reorder data_test_subset columns to match training_features
data_test_subset = data_test[[f for f in training_features if f in data_test.columns]]

# Now, make the prediction
y_pred_final = optimized_xgb.predict(data_test_subset)
y_pred_final

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-174-128d13539418> in <cell line: 9>()
      7 
      8 # Now, make the prediction
----> 9 y_pred_final = optimized_xgb.predict(data_test_subset)
     10 y_pred_final

/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py in predict(self, X, output_margin, validate_features, base_margin, iteration_range)
   1563     ) -> ArrayLike:
   1564         with config_context(verbosity=self.verbosity):
-> 1565             class_probs = super().predict(
   1566                 X=X,
   1567                 output_margin=output_margin,

/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py in predict(self, X, output_margin, validate_features, base_margin, iteration_range)
   1184             if self._can_use_inplace_predict():
   1185                 try:
-> 1186                     predts = self.get_booster().inplace_predict(
   1187                         data=X,
   1188                         iteration_range=iteration_range,

/usr/local/lib/python3.10/dist-packages/xgboost/core.py in inplace_predict(self, data, iteration_range, predict_type, missing, validate_features, base_margin, strict_shape)
   2512             data, fns, _ = _transform_pandas_df(data, enable_categorical)
   2513             if validate_features:
-> 2514                 self._validate_features(fns)
   2515         if _is_list(data) or _is_tuple(data):
   2516             data = np.array(data)

/usr/local/lib/python3.10/dist-packages/xgboost/core.py in _validate_features(self, feature_names)
   3077                 )
   3078 
-> 3079             raise ValueError(msg.format(self.feature_names, feature_names))
   3080 
   3081     def get_split_value_histogram(

ValueError: feature_names mismatch: ['ID', 'Customer_ID', 'Month', 'Name', 'Age', 'SSN', 'Occupation', 'Annual_Income', 'Monthly_Inhand_Salary', 'Num_Bank_Accounts', 'Num_Credit_Card', 'Interest_Rate', 'Num_of_Loan', 'Type_of_Loan', 'Delay_from_due_date', 'Num_of_Delayed_Payment', 'Changed_Credit_Limit', 'Num_Credit_Inquiries', 'Credit_Mix', 'Outstanding_Debt', 'Credit_Utilization_Ratio', 'Credit_History_Age', 'Payment_of_Min_Amount', 'Total_EMI_per_month', 'Amount_invested_monthly', 'Payment_Behaviour', 'Monthly_Balance', 'is_train'] ['Monthly_Inhand_Salary', 'Num_Bank_Accounts', 'Num_Credit_Card', 'Interest_Rate', 'Delay_from_due_date', 'Num_Credit_Inquiries', 'Credit_Utilization_Ratio', 'Total_EMI_per_month']
expected ID, Type_of_Loan, Occupation, Annual_Income, Age, Payment_Behaviour, is_train, Name, Changed_Credit_Limit, Num_of_Loan, Num_of_Delayed_Payment, Credit_History_Age, Monthly_Balance, Outstanding_Debt, Amount_invested_monthly, SSN, Credit_Mix, Customer_ID, Payment_of_Min_Amount, Month in input data

# @title
Credit_Score_to_Label = {
    2:'Good',
    1:'Standard',
    0:'Poor'
}

y_pred_credit_score = np.vectorize(Credit_Score_to_Label.get)(y_pred_final)
y_pred_credit_score

# @title
submission = pd.DataFrame({
    'ID': df_test['ID'],
    'Credit_Score': y_pred_credit_score
})
submission

!jupyter nbconvert --to html "/content/drive/MyDrive/Colab Notebooks/IS675_lab02.ipynb"

# @title
plt.figure(figsize=(8, 6))
sns.countplot(x='Credit_Score', data=submission, palette='pastel')
plt.title('Distribution of Target Variable: Credit_Score on Test')
plt.xlabel('Credit_Score on Test')
plt.ylabel('Count')
plt.show()

	count
Interest_Rate
1	4027
2	3710
3	4153
4	3876
5	7479
6	7089
7	6744
8	7515
9	6747
10	6799
11	6626
12	6828
13	3571
14	3351
15	5984
16	5597
17	5719
18	6154
19	5440

	count
Num_of_Delayed_Payment_Cleaned
26.0	472
27.0	354
28.0	196
3484.0	4
538.0	3
265.0	3
1150.0	3
1014.0	3
2801.0	3
4211.0	3
1946.0	3
2606.0	3
975.0	2
549.0	2
762.0	2

	count
Num_Credit_Inquiries
11.0	8047
12.0	7156
13.0	3545
14.0	2433
15.0	1871
16.0	1107
17.0	672
769.0	5
1460.0	5
1114.0	5

		count
Customer_ID	Credit_History_Age_in_Months
CUS_0x1000	122.0	1
	123.0	1
	124.0	1
	125.0	1
	126.0	1
	127.0	1
	128.0	1
	129.0	1
	130.0	1
	131.0	1
	132.0	1
	133.0	1
CUS_0x1009	365.0	1
	366.0	1
	367.0	1
	369.0	1
	370.0	1
	371.0	1
	372.0	1
	373.0	1

	count
Total_EMI_per_month
0.000000	15615
49.574949	12
16.528703	12
64.443403	12
331.719510	12
...	...
10404.000000	1
15457.000000	1
59578.000000	1
43183.000000	1
33013.000000	1

Load Data¶

Exploratory Data Analysis¶

Reading & Cleaning¶

Business Implications:¶

Business Implications:¶

Exploratory Data Analysis After Cleaning¶

Feature Selection¶

Modeling¶

Resampling¶

Split¶

XGBClassifier¶

RFC try¶

LGBMClassifier¶

	ID	Customer_ID	Month	Name	Age	SSN	Occupation	Annual_Income	Monthly_Inhand_Salary	Num_Bank_Accounts	...	Credit_Mix	Outstanding_Debt	Credit_Utilization_Ratio	Credit_History_Age	Payment_of_Min_Amount	Total_EMI_per_month	Amount_invested_monthly	Payment_Behaviour	Monthly_Balance	Credit_Score
0	0x1602	CUS_0xd40	January	Aaron Maashoh	23	821-00-0265	Scientist	19114.12	1824.843333	3	...	_	809.98	26.822620	22 Years and 1 Months	No	49.574949	80.41529543900253	High_spent_Small_value_payments	312.49408867943663	Good
1	0x1603	CUS_0xd40	February	Aaron Maashoh	23	821-00-0265	Scientist	19114.12	NaN	3	...	Good	809.98	31.944960	NaN	No	49.574949	118.28022162236736	Low_spent_Large_value_payments	284.62916249607184	Good
2	0x1604	CUS_0xd40	March	Aaron Maashoh	-500	821-00-0265	Scientist	19114.12	NaN	3	...	Good	809.98	28.609352	22 Years and 3 Months	No	49.574949	81.699521264648	Low_spent_Medium_value_payments	331.2098628537912	Good
3	0x1605	CUS_0xd40	April	Aaron Maashoh	23	821-00-0265	Scientist	19114.12	NaN	3	...	Good	809.98	31.377862	22 Years and 4 Months	No	49.574949	199.4580743910713	Low_spent_Small_value_payments	223.45130972736786	Good
4	0x1606	CUS_0xd40	May	Aaron Maashoh	23	821-00-0265	Scientist	19114.12	1824.843333	3	...	Good	809.98	24.797347	22 Years and 5 Months	No	49.574949	41.420153086217326	High_spent_Medium_value_payments	341.48923103222177	Good
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
99995	0x25fe9	CUS_0x942c	April	Nicks	25	078-73-5990	Mechanic	39628.99	3359.415833	4	...	_	502.38	34.663572	31 Years and 6 Months	No	35.104023	60.97133255718485	High_spent_Large_value_payments	479.866228	Poor
99996	0x25fea	CUS_0x942c	May	Nicks	25	078-73-5990	Mechanic	39628.99	3359.415833	4	...	_	502.38	40.565631	31 Years and 7 Months	No	35.104023	54.18595028760385	High_spent_Medium_value_payments	496.65161	Poor
99997	0x25feb	CUS_0x942c	June	Nicks	25	078-73-5990	Mechanic	39628.99	3359.415833	4	...	Good	502.38	41.255522	31 Years and 8 Months	No	35.104023	24.02847744864441	High_spent_Large_value_payments	516.809083	Poor
99998	0x25fec	CUS_0x942c	July	Nicks	25	078-73-5990	Mechanic	39628.99	3359.415833	4	...	Good	502.38	33.638208	31 Years and 9 Months	No	35.104023	251.67258219721603	Low_spent_Large_value_payments	319.164979	Standard
99999	0x25fed	CUS_0x942c	August	Nicks	25	078-73-5990	Mechanic	39628.99_	3359.415833	4	...	Good	502.38	34.192463	31 Years and 10 Months	No	35.104023	167.1638651610451	!@9#%8	393.673696	Poor

	ID	Customer_ID	Month	Name	Age	SSN	Occupation	Annual_Income	Monthly_Inhand_Salary	Num_Bank_Accounts	...	Num_Credit_Inquiries	Credit_Mix	Outstanding_Debt	Credit_Utilization_Ratio	Credit_History_Age	Payment_of_Min_Amount	Total_EMI_per_month	Amount_invested_monthly	Payment_Behaviour	Monthly_Balance
0	0x160a	CUS_0xd40	September	Aaron Maashoh	23	821-00-0265	Scientist	19114.12	1824.843333	3	...	2022.0	Good	809.98	35.030402	22 Years and 9 Months	No	49.574949	236.64268203272135	Low_spent_Small_value_payments	186.26670208571772
1	0x160b	CUS_0xd40	October	Aaron Maashoh	24	821-00-0265	Scientist	19114.12	1824.843333	3	...	4.0	Good	809.98	33.053114	22 Years and 10 Months	No	49.574949	21.465380264657146	High_spent_Medium_value_payments	361.44400385378196
2	0x160c	CUS_0xd40	November	Aaron Maashoh	24	821-00-0265	Scientist	19114.12	1824.843333	3	...	4.0	Good	809.98	33.811894	NaN	No	49.574949	148.23393788500925	Low_spent_Medium_value_payments	264.67544623342997
3	0x160d	CUS_0xd40	December	Aaron Maashoh	24_	821-00-0265	Scientist	19114.12	NaN	3	...	4.0	Good	809.98	32.430559	23 Years and 0 Months	No	49.574949	39.08251089460281	High_spent_Medium_value_payments	343.82687322383634
4	0x1616	CUS_0x21b1	September	Rick Rothackerj	28	004-07-5839	_______	34847.84	3037.986667	2	...	5.0	Good	605.03	25.926822	27 Years and 3 Months	No	18.816215	39.684018417945296	High_spent_Large_value_payments	485.2984336755923
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
49995	0x25fe5	CUS_0x8600	December	Sarah McBridec	4975	031-35-0942	Architect	20002.88	1929.906667	10	...	12.0	_	3571.7	34.780553	NaN	Yes	60.964772	146.48632477751087	Low_spent_Small_value_payments	275.53956951573343
49996	0x25fee	CUS_0x942c	September	Nicks	25	078-73-5990	Mechanic	39628.99	NaN	4	...	7.0	Good	502.38	27.758522	31 Years and 11 Months	NM	35.104023	181.44299902757518	Low_spent_Small_value_payments	409.39456169535066
49997	0x25fef	CUS_0x942c	October	Nicks	25	078-73-5990	Mechanic	39628.99	3359.415833	4	...	7.0	Good	502.38	36.858542	32 Years and 0 Months	No	35.104023	__10000__	Low_spent_Large_value_payments	349.7263321025098
49998	0x25ff0	CUS_0x942c	November	Nicks	25	078-73-5990	Mechanic	39628.99	NaN	4	...	7.0	Good	502.38	39.139840	32 Years and 1 Months	No	35.104023	97.59857973344877	High_spent_Small_value_payments	463.23898098947717
49999	0x25ff1	CUS_0x942c	December	Nicks	25	078-73-5990	Mechanic	39628.99	3359.415833	4	...	7.0	_	502.38	34.108530	32 Years and 2 Months	No	35.104023	220.45787812168732	Low_spent_Medium_value_payments	360.37968260123847

	0
ID	0
Customer_ID	0
Month	0
Name	9985
Age	0
SSN	0
Occupation	0
Annual_Income	0
Monthly_Inhand_Salary	15002
Num_Bank_Accounts	0
Num_Credit_Card	0
Interest_Rate	0
Num_of_Loan	0
Type_of_Loan	11408
Delay_from_due_date	0
Num_of_Delayed_Payment	7002
Changed_Credit_Limit	0
Num_Credit_Inquiries	1965
Credit_Mix	0
Outstanding_Debt	0
Credit_Utilization_Ratio	0
Credit_History_Age	9030
Payment_of_Min_Amount	0
Total_EMI_per_month	0
Amount_invested_monthly	4479
Payment_Behaviour	0
Monthly_Balance	1200
Credit_Score	0

	count	unique	top	freq	mean	std	min	25%	50%	75%	max
ID	150000	150000	0x1602	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Customer_ID	150000	12500	CUS_0xd40	12	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Month	150000	12	January	12500	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Name	135000	10139	Stevex	66	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Age	150000	2524	39	4198	NaN	NaN	NaN	NaN	NaN	NaN	NaN
SSN	150000	12501	#F%$D@*&8	8400	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Occupation	150000	16	_______	10500	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Annual_Income	150000	21192	36585.12	24	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Monthly_Inhand_Salary	127500.0	NaN	NaN	NaN	4190.115139	3180.489657	303.645417	1625.265833	3091.0	5948.454596	15204.633333
Num_Bank_Accounts	150000.0	NaN	NaN	NaN	17.00694	117.069476	-1.0	3.0	6.0	7.0	1798.0
Num_Credit_Card	150000.0	NaN	NaN	NaN	22.623447	129.143006	0.0	4.0	5.0	7.0	1499.0
Interest_Rate	150000.0	NaN	NaN	NaN	71.234907	461.537193	1.0	8.0	13.0	20.0	5799.0
Num_of_Loan	150000	623	3	21500	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Type_of_Loan	132888	6260	Not Specified	2112	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Delay_from_due_date	150000.0	NaN	NaN	NaN	21.0634	14.860154	-5.0	10.0	18.0	28.0	67.0
Num_of_Delayed_Payment	139500	1058	19	7949	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Changed_Credit_Limit	150000	4605	_	3150	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Num_Credit_Inquiries	147000.0	NaN	NaN	NaN	28.529014	194.456058	0.0	3.0	6.0	9.0	2597.0
Credit_Mix	150000	4	Standard	54858	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Outstanding_Debt	150000	13622	1360.45	36	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Credit_Utilization_Ratio	150000.0	NaN	NaN	NaN	32.283309	5.113315	20.0	28.054731	32.297058	36.487954	50.0
Credit_History_Age	136500	408	17 Years and 11 Months	628	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Payment_of_Min_Amount	150000	3	Yes	78484	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Total_EMI_per_month	150000.0	NaN	NaN	NaN	1432.513579	8403.759977	0.0	30.947775	71.280006	166.279555	82398.0
Amount_invested_monthly	143250	136497	__10000__	6480	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Payment_Behaviour	150000	7	Low_spent_Small_value_payments	38207	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Monthly_Balance	148238	148224	__-333333333333333333333333333__	15	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Credit_Score	100000	3	Standard	53174	NaN	NaN	NaN	NaN	NaN	NaN	NaN
is_train	150000.0	NaN	NaN	NaN	0.666667	0.471406	0.0	0.0	1.0	1.0	1.0

	0
ID	False
Customer_ID	False
Month	False
Name	True
Age	False
SSN	False
Occupation	False
Annual_Income	False
Monthly_Inhand_Salary	True
Num_Bank_Accounts	False
Num_Credit_Card	False
Interest_Rate	False
Num_of_Loan	False
Type_of_Loan	True
Delay_from_due_date	False
Num_of_Delayed_Payment	True
Changed_Credit_Limit	False
Num_Credit_Inquiries	True
Credit_Mix	False
Outstanding_Debt	False
Credit_Utilization_Ratio	False
Credit_History_Age	True
Payment_of_Min_Amount	False
Total_EMI_per_month	False
Amount_invested_monthly	True
Payment_Behaviour	False
Monthly_Balance	True
Credit_Score	True
is_train	False

	count
Occupation
Lawyer	10644
Engineer	10296
Architect	10236
Mechanic	10164
Scientist	10116
Accountant	10116
Developer	10080
Media_Manager	10080
Teacher	10008
Entrepreneur	9972
Doctor	9852
Journalist	9804
Manager	9648
Musician	9528
Writer	9456

	count
Num_of_Loan
3	21500
2	21423
4	20998
0	15543
1	15112
...	...
291	1
365	1
1014	1
1129_	1
1296_	1

	count
Num_of_Loan_Cleaned
0	16376
1	15901
2	22547
3	22618
4	22111
5	10814
6	11705
7	11024
8	4785
9	5539
23	2
31	2
33	2
42	1
49	2
50	3
53	1
55	2
58	3
95	3

	count
Num_Bank_Accounts
-1	37
0	6494
1	6743
2	6456
3	17905
4	18286
5	18186
6	19505
7	19231
8	19152
9	8181
10	7846
11	28
28	3
34	4
39	3
43	4
70	4
74	5
79	3

	count
Num_Credit_Card
0	29
1	3195
2	3280
3	19816
4	21102
5	27669
6	24802
7	24886
8	7453
9	6976
10	7265
11	77
25	5
28	5
41	5
51	8
66	5
71	5
77	5
92	6

	count
Delay_from_due_date
15	5355
13	5185
8	5004
14	4949
10	4926
...	...
63	90
65	86
-5	51
66	44
67	29

	count
Changed_Credit_Limit
_	3150
11.5	197
11.32	189
8.22	189
7.35	181
10.06	178
8.23	169
7.69	166
7.01	165
11.49	164
7.33	163
9.25	162
3.93	161
1.63	159
8.99	156
8.3	156
7.63	155
8.54	153
8.82	152
7.23	152
9.58	151
11.73	151
10.3	151
8.76	149
9.13	149
11.63	148
11.78	148
11.95	148
9.88	146
8.04	146
9.2	145
7.06	144
7.66	143
10.54	143
8.34	141
7.64	140
4.92	140
4.86	140
9.09	139

	count
Changed_Credit_Limit_Cleaned
11.50	198
8.22	196
11.32	190
7.35	186
7.69	181
...	...
32.11	1
24.27	1
31.15	1
29.20	1
29.17	1

	count
Credit_History_Age_in_Months
215.0	628
220.0	621
219.0	617
237.0	615
218.0	615
...	...
3.0	20
2.0	15
407.0	15
408.0	14
1.0	2

	Customer_ID	Type_of_Loan
32	CUS_0x1cdb	NaN
33	CUS_0x1cdb	NaN
34	CUS_0x1cdb	NaN
35	CUS_0x1cdb	NaN
36	CUS_0x1cdb	NaN
37	CUS_0x1cdb	NaN
38	CUS_0x1cdb	NaN
39	CUS_0x1cdb	NaN
40	CUS_0x95ee	NaN
41	CUS_0x95ee	NaN

	count
Type_of_Loan_Cleaned
No Loan	17112
Not Specified	2340
Credit-Builder Loan	2232
Personal Loan	2232
Student Loan	2208
...	...
Auto Loan, Credit-Builder Loan, Home Equity Loan, Mortgage Loan, Not Specified, Payday Loan, Personal Loan, Student Loan	24
Auto Loan, Credit-Builder Loan, Debt Consolidation Loan, Home Equity Loan, Mortgage Loan, Not Specified, Payday Loan, Student Loan	24
Auto Loan, Debt Consolidation Loan, Home Equity Loan, Mortgage Loan, Payday Loan, Student Loan	24
Credit-Builder Loan, Debt Consolidation Loan, Home Equity Loan, Mortgage Loan, Not Specified, Payday Loan, Personal Loan, Student Loan	12
Auto Loan, Credit-Builder Loan, Debt Consolidation Loan, Mortgage Loan, Not Specified, Payday Loan, Personal Loan	12

	count
Num_of_Delayed_Payment_Cleaned
9.0	7421
8.0	7303
7.0	3571
6.0	3435
5.0	3154

	count
Total_EMI_per_month
10335.0	4
17407.0	3
29766.0	3
26590.0	3
71993.0	3
...	...
53062.0	1
58509.0	1
24556.0	1
78530.0	1
33013.0	1

	count
Amount_invested_monthly
NaN	6750
__10000__	6480
0.0	275
80.41529543900253	1
16.53218878920387	1
...	...
209.17274569312266	1
333.0148085469461	1
274.68712877851107	1
220.58121173366908	1
220.45787812168732	1

	count
Monthly_Balance
NaN	1762
__-333333333333333333333333333__	15
600.1125089726384	1
120.54247208897846	1
784.0174708573453	1
...	...
279.9825060023594	1
260.67157274114965	1
250.4915113003876	1
243.8753153006728	1
360.37968260123847	1

	Customer_ID	Monthly_Balance_Cleaned
5544	CUS_0x9885	423.397
5545	CUS_0x9885	0.000
5546	CUS_0x9885	278.412
5547	CUS_0x9885	420.557
5548	CUS_0x9885	71.288
5549	CUS_0x9885	383.284
5550	CUS_0x9885	229.007
5551	CUS_0x9885	374.031
22720	CUS_0x4379	317.267
22721	CUS_0x4379	290.461